How do you find a clip you shot six months ago?
Four things work, in this order: narrow by date, narrow by camera or card metadata, search the transcript if anyone spoke, and search the picture itself if nobody did. Everything else is scrubbing, and scrubbing is what makes the search take an hour.
Published September 10, 2026 · Field notes
This question comes up almost verbatim in r/editing, and the replies usually turn into a folder-structure argument. That is the wrong conversation. Structure is a filing decision made months ago; finding is a retrieval problem you have right now, and there are only four retrieval methods that do not involve watching your own footage back.
1. Narrow by date, even a rough one
Date is the one attribute that always exists, was never typed by a human and cannot be wrong. Every camera writes it. Every phone writes it. It survives copying, renaming and re-organising, as long as you read the right field.
Read the capture date, not the file date. The creation and modification timestamps that your file browser shows are rewritten by copying to a new drive, by cloud sync, by transcoding and by some backup tools. The capture time lives inside the file: in EXIF for stills, and in the QuickTime or MP4 container metadata for video. On macOS, the Finder's "Date Created" column is often already that value for camera files; on Windows, the Details tab shows "Media created" for MP4 and MOV.
Where it stops: when your guess is a whole year off, or the shoot ran for eleven days and produced 900 clips. Date reduces the pile; it rarely finishes the job on its own.
2. Filter by what the camera wrote
Camera originals carry more than a date. Reel and clip number, camera model, lens and focal length, frame rate, resolution and, on action cameras and phones, GPS coordinates. If you remember that the shot was on the drone, or was slow motion, or was the only day you shot on the 85mm, that is a filter.
Two tools read these fields across a folder tree without importing anything: ExifTool on the command line, and Adobe Bridge or Photo Mechanic if you would rather click. A single ExifTool command can dump every clip's capture time, camera model and duration into a CSV you can sort in a spreadsheet, which is often enough to spot the day you meant.
Where it stops: metadata describes the equipment, not the content. No camera writes down that the shot contains a red bicycle.
3. Search the transcript, if anyone spoke
For interviews, podcasts, streams, meetings and anything with dialogue, transcription is the highest-value move available, because speech is dense and specific in a way that visual descriptions are not. Someone saying "the Rotterdam project" once, three years ago, is findable; a wide shot of a harbour is not, from a transcript.
Whisper-class models now run locally on ordinary laptops, so this no longer means uploading footage. What you get is a text file per clip with timestamps, and text files are searchable by every tool you already own, including grep.
Where it stops: B-roll. A transcript of a silent drone shot is an empty file, and B-roll is exactly what people say they lose. See the difference between searching your footage and transcribing it.
4. Search the picture
The remaining case - no notes, no dialogue, no useful filename - leaves only the image. This is what visual semantic search does: a vision model turns each sampled frame into a vector, your typed description gets turned into a vector by the same model, and the closest matches come back. You type "man walking in the rain" and get clips of a man walking in the rain, regardless of what the file is called.
Two things are worth understanding before you rely on it. First, it matches the gist of a frame, so it is good at subject, setting, colour and time of day, and much weaker on fine detail, text on signs, and which specific person is on screen. Second, it works on sampled frames rather than on continuous video, so anything that happens between the samples is not in the index.
What Reelary does here, concretely
Reelary is our tool, so treat this section as disclosure rather than a recommendation. It indexes a folder by sampling frames - at shot boundaries where it can detect them, otherwise on a 2-second grid - embedding each sampled frame with an on-device vision model, and storing the vectors in a local database. Searching returns continuous ranges rather than single frames: adjacent matching samples inside one video are merged into a clip with a start and end time, up to three separate ranges per video.
Two numbers that set expectations, both from the code rather than from marketing: sampling is every 2 seconds, and there is a hard cap of 16 sampled frames per video. A 40-second clip is covered densely. A 90-minute conference recording gets 16 samples across the whole thing, which is a thumbnail-level summary, not a searchable transcript of the picture.
When not to use Reelary for this.
If the clip you are looking for is defined by something somebody said, use a transcription tool - Reelary's search is visual, and its Whisper-based transcription produces captions for the cut you are building, not a searchable index of your library. If you need to find a specific person by face across an archive, we do not do face recognition; Jumper and DaVinci Resolve Studio do. And if the material is one long unbroken recording rather than many clips, the 16-frame cap means you will get a summary, not coverage - cut it into scenes first, or use a transcript.
The honest summary
- Date always works, never finishes the job.
- Camera metadata is free and precise, but says nothing about content.
- Transcript is the best method when there is speech, and useless when there is not.
- Visual search is the only method that works on silent B-roll with a meaningless filename, and it trades detail for coverage.
Most libraries need two of the four. Almost nobody needs a better folder structure, which is the answer the thread usually settles on.
Questions people ask about this
What is the fastest way to find one old clip?
Narrow by date first. Even a rough guess - "spring, probably before the Berlin trip" - cuts a library by an order of magnitude in seconds, and every other method works better on a smaller set. Sort by capture date rather than file modification date, because editing, copying and cloud sync all rewrite modification dates.
Does Windows or macOS search find things inside video files?
No. Windows Search and macOS Spotlight index filenames and, for some formats, embedded metadata and text documents. Neither one looks at the picture inside an MP4 or MOV, so a search for "sunset" only matches clips with the word sunset in the name or in a metadata field somebody filled in.
What if I never wrote anything down about the shoot?
Then only two signals survive: the metadata the camera wrote automatically - date, time, reel, camera model, sometimes GPS - and the picture itself. Everything else depends on notes you did not take. This is the specific case visual search is for.