Get similarity score/rank for a subset of N candidates

Context:
You have built your index using all of your candidate & query embeddings.
And now you want to get the similarity score between either:

• A single user (1021314) & a subset list of N candidates ['TopGun', 'Star Wars', 'The Titantic']

There is a requirement to do this fast so batch/vectorized predictions are required.
Aside: This is a pattern I've seen a need for several times so just flagging here in case it's helpful.

Question: What is the best way to do this?

Options considered

1. Exclude all candidates except your N=3 candidates your concerned about:

for row in ratings.batch(300).take(1):
  top_movies = brute_force.query_with_exclusions(row, exclusions=row['exclusions'])[1].numpy()

2. Manually extract the user & candidate embeddings from the model and just handle this myself via simple dot products or cosine similarity between the embeddings *[errorr prone] *
3. Just get the scores/rank for all of my candidates and filter results by index lookup after the fact [very slow]

Option 1 feels cleanest as it handles all vocabulary/index mappings and leaves less room for error.
The one downside is that when I have a large number of candidates (e.g. 500,000) it means I have to have a row in my dataset with (500,000 - N(3)) candidates to exclude for each row.

I guess I was wondering if it is possible to instead of specifying exclusions to actually specify a subset of candidates to consider/include for each row/prediction. This would help us out a huge amount with several models we have in production (e.g. Find the most similar "best fitting" sku size for a user for a given fashion item ----> subset of candidates is N=14 in this case rather than our entire inventory)

I hope this all makes sense!