Semantic vs Keyword Search on Mac: What’s the Difference and Why It Matters

Most people discover Fenn because they want smarter search on their Mac. They’re tired of Spotlight missing the obvious, or showing them a wall of filenames when they barely remember what they saved.

Fenn fixes that with semantic search: it understands meaning, not just keywords. You can type things like "dog wearing a party hat" or "slide deck with pink neon typography" and get exactly what you meant, even if those words never appear in a filename.

But here’s the thing.

Fenn isn’t just a semantic search engine.

It also supports precise, keyword-based search. Because sometimes, the fastest way to find what you need is to match exactly what you typed. Especially if you're looking for something like "EC10405" buried in a spreadsheet inside your Downloads folder.

Let’s break it down.

Keyword Search: When Specifics Matter

If you’re hunting for a unique code, a filename, or a very specific string like "EC10405", keyword search is your best friend. Fenn lets you toggle keyword-based results, so you can quickly scan for exact matches across your entire local file system.

That includes:

• Sheet names inside Excel files

• Text layers inside PDFs or Slides

• Filenames or internal metadata

• Code snippets, document IDs, SKUs

Spotlight tries to do this too, but often falls short when indexing breaks or metadata gets skipped. Fenn’s keyword engine is purpose-built to dig deeper and stay consistent, even across messy, unsorted folders.

Looking for a deeper comparison? We unpack these differences in our guide: Spotlight Mac: The Ultimate Guide (and Why You Might Need an Alternative)

Semantic Search: When You Remember the Idea, Not the Words

But what if you don’t remember an exact phrase?

What if all you remember is the concept?

This is where Fenn’s semantic engine shines. You type:

• “voice note about startup burnout”

• “screenshot of a French flag”

• “Zoom call where pricing options were discussed”

Fenn reads inside your files using vision, OCR, speech-to-text, and context. It indexes scenes from videos, passages in PDFs, slides from decks, and even words spoken aloud in voice memos. It surfaces what you meant, not just what you typed.

We go deeper into this approach in What Is Semantic Search? And Why Your Mac Needs It in 2025

Choosing the Right Mode: Semantic, Keyword, or Both

The good news? Fenn doesn’t make you choose just one type of search forever. You can switch modes based on your use case.

When you open Fenn search bar, in Advanced Settings you can choose to:

• Prioritize semantic results

• Show keyword matches first

• Or combine both, letting Fenn rank them side-by-side

This flexibility means you can adapt how you search depending on the task at hand.

Looking for a sentence someone spoke during a podcast? Semantic.

Searching for a document that contains “EC10405”? Keyword.

Looking for either one? Use both.

Fine-Tune Your Search with Adjustable Weights

Here’s something power users love: Search Weights.

In Fenn, you can tweak how the engine prioritizes different types of content. You’ll see sliders for:

• OCR (for text inside images and scanned documents)

• Vision (for visual elements in photos or videos)

• Text (for traditional written content)

• Audio (for speech in recordings or calls)

Let’s say you remember seeing a line of text in a YouTube video you downloaded. You bump the OCR weight up. Fenn will focus more on scanned or visual text inside the video frames.

Or maybe you’re trying to find a phrase someone said in a Zoom call. Boost the Audio weight, and Fenn will prioritize transcribed voice content from your recordings.

These weights are incredibly useful in helping Fenn focus its energy where it matters most. They’re not about making the search slower or faster—they’re about making the results more relevant to your memory.

Real-World Example: Flags, Pages, and PDF Chaos

Here’s a perfect case.

You vaguely remember a PDF you read last month that had a flag of France somewhere inside it. That’s all you’ve got.

• Keyword search won’t help. “France” might appear in a bunch of files.

• Low-intensity semantic search will show you the right PDF, but not the exact spot.

• Medium-intensity semantic + OCR search, with a boost to Vision and OCR weights, will find the exact page with the image of the flag and highlight it directly.

That’s what we mean when we talk about “search that understands your memory.” This isn’t about finding a file. It’s about finding the moment.

You can explore this concept more in How Fenn Helps You Find the Needle in a Haystack

Why This Matters

Fenn is built for how you actually work. Sometimes you want speed. Sometimes you need precision. Sometimes you just want a search engine that understands what you’re talking about without needing perfect input.

With Fenn, you get the best of both worlds:

• Keyword accuracy when you need it

• Semantic intelligence when you don’t

• Search weights for complete control

You’re not locked into a single way of thinking. You can switch modes, tweak relevance, and get back to work faster.

Final Thought

We built Fenn because traditional search on macOS is broken. Spotlight is stuck in the past. Finder isn’t built for scale. Most other tools guess at what you meant—or ask you to remember exact filenames.

Fenn doesn’t do that. It adapts to you.

And now with both semantic and keyword search, plus custom weights, it gives you the control you need to search your Mac the way you actually think.

Ready to see the difference?

👉 Try Fenn now and find what you meant, not just what you typed.