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Multimedia data, particularly, video data, has dominated peer-to-peer (P2P) networks. Therefore, it is demanding to provide content based retrieval in P2P networks. Similarity matching is one of the challenging issues. In this paper, we present a novel two-step method to reduce computational complexity of similarity matching in P2P networks. In the first step, an efficient maximum matching (MM) technique is employed to obtain an initial set of similar video candidates. In the second step, these candidates are further selected with a more accurate, but more computationally expensive optimal matching (OM) technique. In order to further improve the computational efficiency of the proposed method, four other matching techniques are proposed to replace MM technique. Various experimental results indicate that the proposed approach is more effective for CBVR while achieving significantly computational saving.