Content-Based Video Copy Detection (CBVCD) consists of detecting and retrieving videos that are copies of known original videos. CBVCD systems rely on two different tasks: Feature Extraction task, that calculates many representative descriptors for a video sequence, and Similarity Search task, that is the algorithm for finding videos in an indexed collection that match a query video. This paper describes P-VCD, which is a novel approach for CBVCD based on global de scriptors, weighted combinations of distances, a pivot-based index structure, an approximate similarity search, and a voting algorithm for copy localization. P-VCD was tested at the TRECVID 2010 evaluation, where it was the best positioned CBVCD system for Balanced and No False Alarms profiles considering visual-only runs (and above the median considering all runs). P-VCD shows that by using approximate similarity searches one can obtain good effectiveness, and that global descriptors can achieve competitive results with TRECVID transformations.