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This paper describes VFerret, a content-based similarity search tool for continuous archived video. Instead of depending on attributes or annotations to search desired data from long-time archived video, our system allows users to perform content-based similarity search using visual and audio features, and to combine content-based similarity search with traditional search methods. Our preliminary experience and evaluation shows that content-based similarity search is easy to use and can achieve 0.79 average precision on our simple benchmark. The system is constructed using Ferret toolkit and its memory footprint for metadata is quite small, requiring about 1.4Gbytes for one year of continuous archived video data.