How to Filter Contains One Of Many in Excel

Why This Task Matters in Excel

Have you ever received a long customer-feedback sheet and been asked to send your manager every comment that mentions “delay”, “refund”, or “broken”? Or maybe you oversee sales data and you need to pull out every row where the product description includes “XL”, “large pack”, or “gift bundle”. These situations occur daily across industries: customer service, sales, logistics, R&D, compliance, journalism, and more. The common theme is simple—users must quickly isolate records whose text contains any one of several keywords.

In business, time is money. Manually scanning thousands of rows is not just tedious, it is error-prone and unsustainable when the data refreshes every day. Excel’s power lies in its ability to automate such tasks so that decisions are faster and more reliable. Filtering on a single word is straightforward with standard AutoFilter, but filtering on “any of many” words without missing something or double-counting is considerably harder. Without an efficient technique, analysts often resort to temporary helper columns, multiple passes of filtering, or clunky VBA macros—all of which add maintenance overhead and introduce new failure points.

Learning to “filter contains one of many” helps you:

• Accelerate data clean-up for dashboards and ad-hoc reporting
• Provide on-demand filtered exports to downstream systems or colleagues
• Avoid expensive BI licenses for simple keyword extractions
• Combine with PivotTables, charts, Power Query, or Power Automate for complete, self-updating workflows

Excel offers several approaches that scale from beginner to advanced: the Dynamic Array FILTER function, legacy Advanced Filter, AutoFilter with wildcards, helper-column formulas (SEARCH, FIND, COUNTIF), and Power Query’s text filters. Selecting the best tool depends on Excel version, data refresh frequency, and user skill level. Mastering at least one dynamic, formula-based method is essential because it eliminates repetitive clicking and keeps filtering rules transparent.

Failing to learn this skill leads to delayed insights, frustrated stakeholders, and potentially lost revenue if critical issues stay hidden in unfiltered data. Just as importantly, the logic you’ll practice here—array manipulation, boolean algebra, and text functions—strengthens your broader Excel competency, preparing you for more complex data-wrangling tasks like fuzzy matching, sentiment analysis, and rule-based alerting.

Best Excel Approach

For users on Microsoft 365 or Excel 2021, the most efficient method is a single dynamic-array formula that combines FILTER, SEARCH, and BYROW (or the older MMULT pattern). It requires no helper columns, updates automatically when new data arrives, and is fully transparent for audit purposes.

Conceptually, the formula asks:
“For each row’s text cell, does any keyword in the list appear inside it? If yes, return that row.”

Key reasons this approach is best:

• Purely formula-based—no clicks needed after initial setup
• Instant recalculation on data change or keyword change
• Works inside structured tables, making column references readable
• Spill range keeps output neatly grouped without blanks

Here is the most universally readable syntax using BYROW and LAMBDA (Office 365):

=FILTER(
    DataTable,                                  /* entire table to return */
    BYROW(DataTable[Comment],                   /* evaluate each comment row */
        LAMBDA(rowTxt,
            SUM(--ISNUMBER(SEARCH($F$2:$F$10, rowTxt))) )   /* any hit? */
    ) > 0
)

Explanation of parameters:

• DataTable – the named table containing full rows you want returned.
• DataTable[Comment] – the specific column you want searched.
• $F$2:$F$10 – a vertical list of keywords (can be dynamic).
• SEARCH – performs case-insensitive find and returns position or error.
• ISNUMBER – converts found positions to TRUE/FALSE.
• SUM(– ) – converts TRUE/FALSE to 1/0 and totals them.
• > 0 – final boolean test passed to FILTER.

If your version lacks BYROW or LAMBDA, use the classic MMULT array technique:

=FILTER(
    DataTable,
    MMULT(--ISNUMBER(SEARCH($F$2:$F$10, DataTable[Comment])), TRANSPOSE(COLUMN($F$2:$F$10)^0)) > 0
)

Both formulas achieve the same result; choose the one supported by your Excel build.

Parameters and Inputs

Before writing the formula, verify the following inputs:

1. Data range or table

   • Should be contiguous; convert to an Excel Table (Ctrl+T) for easier referencing.
   • Ensure text column (e.g., Comment, Description, Notes) contains actual text, not formulas that sometimes return errors.

2. Keyword list

   • Vertical list in a dedicated column or spill range.
   • Avoid leading/trailing spaces—use TRIM or manual clean-up.
   • Decide whether the search should be case-sensitive. If yes, replace SEARCH with FIND.

3. Optional columns to return

   • FILTER can output entire rows (DataTable) or selected columns (DataTable[[Column1]:[Column3]]).
   • Keep output structure consistent if downstream formulas reference specific columns by position.

4. Empty-string or error handling

   • Add a third argument to FILTER such as "No matches" to avoid spill errors when nothing is found.

5. Edge cases

   • Overlapping keywords (e.g., “pro” and “product”) may cause unintended matches; refine list or use word boundaries.
   • Large datasets (100k+ rows) need 64-bit Excel for better memory handling.
   • Non-text cells in the target column will trigger errors; wrap the target column in TEXT or pre-filter blanks.

Step-by-Step Examples

Example 1: Basic Scenario

Imagine a simple complaints log. In [A1:D12] you have a table named Complaints with columns: Date, Customer, OrderID, Comment. You want to isolate comments that mention any of three issues: “refund”, “late”, “damaged”.

1. Type the keywords vertically in [F2:F4].

• F2: refund
• F3: late
• F4: damaged

2. Place your cursor in [H2]. Enter:

=FILTER(
    Complaints,
    BYROW(Complaints[Comment],
        LAMBDA(txt, SUM(--ISNUMBER(SEARCH($F$2:$F$4, txt))))
    ) > 0,
    "No matching comments"
)

3. Hit Enter. The formula spills, returning matching rows.

Why it works: SEARCH checks each keyword in $F$2:$F$4 against each comment. ISNUMBER converts found positions to TRUE (1). SUM totals the 1s for each row. If the total is greater than 0, that row passes the filter. FILTER then returns the rows from Complaints.

Variations:

• Case-sensitive? Replace SEARCH with FIND.
• Prevent partial word hits (“later” matching “late”)? Surround keywords with spaces or use =" "&keyword&" " plus SEARCH.
  Troubleshooting: If output shows “No matching comments” despite obvious hits, check for stray spaces or different text casing.

Example 2: Real-World Application

Suppose you’re part of an e-commerce analytics team handling a 25 000-row Sales table with columns: Date, SKU, ProductName, Marketplace, Units, Revenue. Marketing requests a quick snapshot of any product names that reference “bundle”, “mega”, “XL”, or “gift” and only for Amazon sales.

Data Preparation:

• Convert [A1:G25001] to a table named Sales.
• Place keywords in Keywords[Term] table for maintainability.
• Use an additional filter condition for Marketplace = \"Amazon\".

Formula (entered in [J2]):

=FILTER(
    Sales,
    (Sales[Marketplace]="Amazon") *
    (BYROW(Sales[ProductName],
        LAMBDA(pn, SUM(--ISNUMBER(SEARCH(Keywords[Term], pn))))
     ) > 0),
    "No Amazon bundles found"
)

Step walk-through:

1. The first logical test (Sales[Marketplace]="Amazon") produces TRUE/FALSE for every row, 1 for Amazon.
2. The BYROW section does the keyword search across ProductName.
3. Multiplying the two booleans performs an AND operation—only rows that are Amazon AND contain any keyword survive.
4. The resulting matrix flows into FILTER, returning full rows.

Business Impact: This single formula means marketing can add or remove keywords in Keywords and instantly see revised results without bringing the file back to you. It also prevents errors that arise when marketing interns try to stack manual text filters.

Performance tips:

• For 25 000 rows, calculation time is negligible on modern hardware.
• If you scale to 500 000 rows, consider Power Query or database push-down to avoid Excel’s row limit.

Example 3: Advanced Technique

Scenario: A compliance officer needs to review internal chat messages for any of 100 sensitive phrases across a 100 000-row log. You also need to highlight which specific keyword matched each row and flag multiple hits.

Setup:

• Chat log table Chats with fields: TimeStamp, User, Message.
• Keywords table Terms with ID and Phrase columns.
• You need three outputs: filtered rows, first matching term, and count of matches per row.

Step 1 – Count of matches (helper column inside Chats, call it HitCount):

=SUM(--ISNUMBER(SEARCH(Terms[Phrase], [@Message])))

This array formula spills automatically in a structured reference context.

Step 2 – First matching term (helper column FirstHit):

=INDEX(
    Terms[Phrase],
    XMATCH(TRUE, ISNUMBER(SEARCH(Terms[Phrase], [@Message])))
)

Step 3 – Final filtered report:

=FILTER(
    Chats[[TimeStamp]:[Message]],
    Chats[HitCount] > 0
)

Professional considerations:

• Avoid 32-bit Excel—memory errors are common with 100k rows.
• Protect the keyword list; accidental deletions break formulas.
• For ultra-large logs, use Power Query: apply Text.ContainsAny to filter then load only matched rows back to Excel.

Error handling: Wrap XMATCH in IFERROR to avoid spill errors when no term is found. The combined technique produces a robust compliance dashboard identifying the offending phrase and quantifying severity.

Tips and Best Practices

1. Convert raw data to Excel Tables—structured references make formulas readable and automatically expand with new rows.
2. Store keywords in a dedicated named range or Table. Business users can edit lists without touching formulas.
3. Use sheet-level protection to lock formulas while keeping the keyword list editable.
4. Replace SEARCH with FIND for case-sensitive searches. Document this decision so later users understand the behavior.
5. For repeated reporting, pair the dynamic filter with a PivotTable connected to the spill range; the pivot refreshes when the spill updates.
6. Benchmark large files: If recalculation exceeds a few seconds, consider Power Query or exporting to a database with full-text search capabilities.

Common Mistakes to Avoid

1. Forgetting absolute references. When the keyword list scrolls off-screen, relative references change and formulas break. Always lock: $F$2:$F$10.
2. Ignoring hidden spaces. A leading space in “ refund” prevents SEARCH from finding it. Trim your lists or wrap TRIM around the keyword column.
3. Overlapping keywords. Having both “pro” and “product” causes false positives. Use more specific phrases or word boundary logic.
4. Not accounting for blanks or errors in the target column. SEARCH on blank cells returns errors; either pre-filter blanks or wrap the column in IFERROR(txt,"").
5. Relying on AutoFilter for dynamic reports. Manual filters are forgotten and not saved properly in shared files—use formulas for repeatability.

Alternative Methods

When to use which?

• Office 365 users: start with dynamic FILTER.
• Shared corporate environment on mixed versions: helper column + AutoFilter.
• Massive logs: Power Query or external database.

FAQ

When should I use this approach?

Use a dynamic FILTER when you need a live subset of data that updates automatically as either the data or keyword list changes. Ideal for dashboards, compliance monitoring, and any ad-hoc report refreshing multiple times a day.

Can this work across multiple sheets?

Yes. Set the data table on Sheet1 and the keyword list on Sheet2. Reference them with sheet-qualified structured references like Sheet1!DataTable[Comment] and lock the keyword range with `

How to Filter Contains One Of Many in Excel

Why This Task Matters in Excel

Have you ever received a long customer-feedback sheet and been asked to send your manager every comment that mentions “delay”, “refund”, or “broken”? Or maybe you oversee sales data and you need to pull out every row where the product description includes “XL”, “large pack”, or “gift bundle”. These situations occur daily across industries: customer service, sales, logistics, R&D, compliance, journalism, and more. The common theme is simple—users must quickly isolate records whose text contains any one of several keywords.

In business, time is money. Manually scanning thousands of rows is not just tedious, it is error-prone and unsustainable when the data refreshes every day. Excel’s power lies in its ability to automate such tasks so that decisions are faster and more reliable. Filtering on a single word is straightforward with standard AutoFilter, but filtering on “any of many” words without missing something or double-counting is considerably harder. Without an efficient technique, analysts often resort to temporary helper columns, multiple passes of filtering, or clunky VBA macros—all of which add maintenance overhead and introduce new failure points.

Learning to “filter contains one of many” helps you:

• Accelerate data clean-up for dashboards and ad-hoc reporting
• Provide on-demand filtered exports to downstream systems or colleagues
• Avoid expensive BI licenses for simple keyword extractions
• Combine with PivotTables, charts, Power Query, or Power Automate for complete, self-updating workflows

Excel offers several approaches that scale from beginner to advanced: the Dynamic Array FILTER function, legacy Advanced Filter, AutoFilter with wildcards, helper-column formulas (SEARCH, FIND, COUNTIF), and Power Query’s text filters. Selecting the best tool depends on Excel version, data refresh frequency, and user skill level. Mastering at least one dynamic, formula-based method is essential because it eliminates repetitive clicking and keeps filtering rules transparent.

Failing to learn this skill leads to delayed insights, frustrated stakeholders, and potentially lost revenue if critical issues stay hidden in unfiltered data. Just as importantly, the logic you’ll practice here—array manipulation, boolean algebra, and text functions—strengthens your broader Excel competency, preparing you for more complex data-wrangling tasks like fuzzy matching, sentiment analysis, and rule-based alerting.

Best Excel Approach

For users on Microsoft 365 or Excel 2021, the most efficient method is a single dynamic-array formula that combines FILTER, SEARCH, and BYROW (or the older MMULT pattern). It requires no helper columns, updates automatically when new data arrives, and is fully transparent for audit purposes.

Conceptually, the formula asks:
“For each row’s text cell, does any keyword in the list appear inside it? If yes, return that row.”

Key reasons this approach is best:

• Purely formula-based—no clicks needed after initial setup
• Instant recalculation on data change or keyword change
• Works inside structured tables, making column references readable
• Spill range keeps output neatly grouped without blanks

Here is the most universally readable syntax using BYROW and LAMBDA (Office 365):

CODE_BLOCK_0

Explanation of parameters:

• DataTable – the named table containing full rows you want returned.
• DataTable[Comment] – the specific column you want searched.
• $F$2:$F$10 – a vertical list of keywords (can be dynamic).
• SEARCH – performs case-insensitive find and returns position or error.
• ISNUMBER – converts found positions to TRUE/FALSE.
• SUM(– ) – converts TRUE/FALSE to 1/0 and totals them.
• > 0 – final boolean test passed to FILTER.

If your version lacks BYROW or LAMBDA, use the classic MMULT array technique:

CODE_BLOCK_1

Both formulas achieve the same result; choose the one supported by your Excel build.

Parameters and Inputs

Before writing the formula, verify the following inputs:

1. Data range or table

   • Should be contiguous; convert to an Excel Table (Ctrl+T) for easier referencing.
   • Ensure text column (e.g., Comment, Description, Notes) contains actual text, not formulas that sometimes return errors.

2. Keyword list

   • Vertical list in a dedicated column or spill range.
   • Avoid leading/trailing spaces—use TRIM or manual clean-up.
   • Decide whether the search should be case-sensitive. If yes, replace SEARCH with FIND.

3. Optional columns to return

   • FILTER can output entire rows (DataTable) or selected columns (DataTable[[Column1]:[Column3]]).
   • Keep output structure consistent if downstream formulas reference specific columns by position.

4. Empty-string or error handling

   • Add a third argument to FILTER such as "No matches" to avoid spill errors when nothing is found.

5. Edge cases

   • Overlapping keywords (e.g., “pro” and “product”) may cause unintended matches; refine list or use word boundaries.
   • Large datasets (100k+ rows) need 64-bit Excel for better memory handling.
   • Non-text cells in the target column will trigger errors; wrap the target column in TEXT or pre-filter blanks.

Step-by-Step Examples

Example 1: Basic Scenario

Imagine a simple complaints log. In [A1:D12] you have a table named Complaints with columns: Date, Customer, OrderID, Comment. You want to isolate comments that mention any of three issues: “refund”, “late”, “damaged”.

1. Type the keywords vertically in [F2:F4].

• F2: refund
• F3: late
• F4: damaged

2. Place your cursor in [H2]. Enter:

CODE_BLOCK_2

3. Hit Enter. The formula spills, returning matching rows.

Why it works: SEARCH checks each keyword in $F$2:$F$4 against each comment. ISNUMBER converts found positions to TRUE (1). SUM totals the 1s for each row. If the total is greater than 0, that row passes the filter. FILTER then returns the rows from Complaints.

Variations:

• Case-sensitive? Replace SEARCH with FIND.
• Prevent partial word hits (“later” matching “late”)? Surround keywords with spaces or use =" "&keyword&" " plus SEARCH.
  Troubleshooting: If output shows “No matching comments” despite obvious hits, check for stray spaces or different text casing.

Example 2: Real-World Application

Suppose you’re part of an e-commerce analytics team handling a 25 000-row Sales table with columns: Date, SKU, ProductName, Marketplace, Units, Revenue. Marketing requests a quick snapshot of any product names that reference “bundle”, “mega”, “XL”, or “gift” and only for Amazon sales.

Data Preparation:

• Convert [A1:G25001] to a table named Sales.
• Place keywords in Keywords[Term] table for maintainability.
• Use an additional filter condition for Marketplace = \"Amazon\".

Formula (entered in [J2]):

CODE_BLOCK_3

Step walk-through:

1. The first logical test (Sales[Marketplace]="Amazon") produces TRUE/FALSE for every row, 1 for Amazon.
2. The BYROW section does the keyword search across ProductName.
3. Multiplying the two booleans performs an AND operation—only rows that are Amazon AND contain any keyword survive.
4. The resulting matrix flows into FILTER, returning full rows.

Business Impact: This single formula means marketing can add or remove keywords in Keywords and instantly see revised results without bringing the file back to you. It also prevents errors that arise when marketing interns try to stack manual text filters.

Performance tips:

• For 25 000 rows, calculation time is negligible on modern hardware.
• If you scale to 500 000 rows, consider Power Query or database push-down to avoid Excel’s row limit.

Example 3: Advanced Technique

Scenario: A compliance officer needs to review internal chat messages for any of 100 sensitive phrases across a 100 000-row log. You also need to highlight which specific keyword matched each row and flag multiple hits.

Setup:

• Chat log table Chats with fields: TimeStamp, User, Message.
• Keywords table Terms with ID and Phrase columns.
• You need three outputs: filtered rows, first matching term, and count of matches per row.

Step 1 – Count of matches (helper column inside Chats, call it HitCount):

CODE_BLOCK_4

This array formula spills automatically in a structured reference context.

Step 2 – First matching term (helper column FirstHit):

CODE_BLOCK_5

Step 3 – Final filtered report:

CODE_BLOCK_6

Professional considerations:

• Avoid 32-bit Excel—memory errors are common with 100k rows.
• Protect the keyword list; accidental deletions break formulas.
• For ultra-large logs, use Power Query: apply Text.ContainsAny to filter then load only matched rows back to Excel.

Error handling: Wrap XMATCH in IFERROR to avoid spill errors when no term is found. The combined technique produces a robust compliance dashboard identifying the offending phrase and quantifying severity.

Tips and Best Practices

1. Convert raw data to Excel Tables—structured references make formulas readable and automatically expand with new rows.
2. Store keywords in a dedicated named range or Table. Business users can edit lists without touching formulas.
3. Use sheet-level protection to lock formulas while keeping the keyword list editable.
4. Replace SEARCH with FIND for case-sensitive searches. Document this decision so later users understand the behavior.
5. For repeated reporting, pair the dynamic filter with a PivotTable connected to the spill range; the pivot refreshes when the spill updates.
6. Benchmark large files: If recalculation exceeds a few seconds, consider Power Query or exporting to a database with full-text search capabilities.

Common Mistakes to Avoid

1. Forgetting absolute references. When the keyword list scrolls off-screen, relative references change and formulas break. Always lock: $F$2:$F$10.
2. Ignoring hidden spaces. A leading space in “ refund” prevents SEARCH from finding it. Trim your lists or wrap TRIM around the keyword column.
3. Overlapping keywords. Having both “pro” and “product” causes false positives. Use more specific phrases or word boundary logic.
4. Not accounting for blanks or errors in the target column. SEARCH on blank cells returns errors; either pre-filter blanks or wrap the column in IFERROR(txt,"").
5. Relying on AutoFilter for dynamic reports. Manual filters are forgotten and not saved properly in shared files—use formulas for repeatability.

Alternative Methods

When to use which?

• Office 365 users: start with dynamic FILTER.
• Shared corporate environment on mixed versions: helper column + AutoFilter.
• Massive logs: Power Query or external database.

FAQ

When should I use this approach?

Use a dynamic FILTER when you need a live subset of data that updates automatically as either the data or keyword list changes. Ideal for dashboards, compliance monitoring, and any ad-hoc report refreshing multiple times a day.

Can this work across multiple sheets?

Yes. Set the data table on Sheet1 and the keyword list on Sheet2. Reference them with sheet-qualified structured references like Sheet1!DataTable[Comment] and lock the keyword range with . The spill output can even be on a third sheet.

What are the limitations?

FILTER is available only in Microsoft 365 and Excel 2021. The formula limit is roughly two million cells—exceeding that may throw a spill error. Additionally, SEARCH is case-insensitive, so substitute FIND if you require case sensitivity.

How do I handle errors?

Wrap your FILTER call with an error handler as the third argument, e.g., FILTER(...,"No matches"). For errors inside the search step (usually due to non-text cells), pre-clean the column or embed IFERROR(SEARCH(...),FALSE).

Does this work in older Excel versions?

You cannot use FILTER before Office 365/2021, but you can replicate the logic with a helper COUNTIF column combined with AutoFilter, or use Advanced Filter with the “contains” criterion and wildcards like *refund*.

What about performance with large datasets?

For up to 100 000 rows, dynamic arrays calculate nearly instantaneously. Beyond that, switch calculation mode to Manual during heavy edits or offload to Power Query. On 64-bit Excel, memory is usually the bottleneck, so consider splitting files or archiving historic data.

Conclusion

Being able to filter rows that contain any of several keywords is a deceptively powerful Excel skill that saves time, improves accuracy, and unlocks streamlined workflows. Whether you choose the modern dynamic-array approach or a legacy-compatible workaround, mastering the logic behind keyword matching prepares you for more advanced data-manipulation tasks. Practice the examples, adapt them to your own datasets, and you’ll soon find yourself solving text-filtering challenges that once seemed impossible. Keep exploring related functions like TEXTSPLIT, Power Query, and dynamic arrays to continue leveling up your Excel toolkit.