The demand of video copy detection system is growing rapidly, as the development of online video uploading and sharing. In the past two decades, the research concentrates on the video information extraction or feature building rather than the fast searching strategy. However, as the number of videos is growing, fast searching in video copy detection systems has become a big issue. In this paper, we propose a novel fast searching strategy for Inverted Index File (IIF) based video copy detection system by using fingerprinting technology. The proposed searching approach consists of two parts - fingerprint matching and video fragment matching. To speed up the fingerprint matching process, a table lookup operation is utilized that rely on the counting of matched sub-fingerprints instead of Hamming Distance metric. For video fragment matching, all fingerprint candidate is used to propose more than one matched video candidate with a different similarity score. The proposed fast searching strategy is tested on experimental content-based video copy detection system with different fingerprinting methods, distortion types and video database scale. Experimental results show that the proposed searching approach achieve high accuracy, and is around 10 times faster compared with the conventional IIF method. Moreover, with database upscaling, the searching rate of the proposed approach is faster than the conventional IIF methods that further make it a potential candidate to be used in large-scale video copy detection systems.