The problem we are considering here is duplicate video detection. We have a database of N videos and we store compact signatures, called fingerprints, for each of them. When a query video is presented, the system first returns the top-K most closely matched videos. Then, a more detailed search is performed among the top-K retrieved model videos to obtain the best match. Finally, a separate module is used to confirm whether the best matched video is indeed a duplicate. In this write-up, we focus on the VQ based pruned search where the effort is to return the top-K neighbors in the fastest possible manner, without having to do a linear scan of all the N database signatures.