In a similar senario with binary vectors (recommender systems in my case which can be see as a special case of information retrieval) I had very good success with cosine similarity.
When measuring Rankscore I got much better rate on my retrievals when I used Cosine similarity than with the Tanimoto coefficient, but this might be very application specific. You should probably experiment with different similarity measurements and see what yields the best result in you application.
I compare the result of the effects from different similarity measures between my binary vectors here. As mentioned above I have the greatest success with cosine similarity.