The exponential growth of visual data, driven by advancements in computing, storage, and networking technologies, underscores the need for more precise methods and techniques for efficient retrieval and management. A Content-Based Retrieval (CBR) system facilitates the retrieval of media, including images, videos, and audio, from a store by analyzing their content rather than relying on metadata such as descriptions, titles, or textual annotations. Forged videos are rapidly increasing, undermining trust in video content and posing significant security risks. Various performance metrics are employed to assess the confidence level of CBR systems. Furthermore, to effectively retrieve forged videos corresponding to a given original video, it is essential to assess the relevance of the evaluation metric. In this paper, we introduce a novel evaluation metric for CBR systems, termed the Weighted Distance-Based Score and Normal Precision Pair (WDBSP). This metric leverages distance values as weights to assess retrieval performance while simultaneously measuring precision. WDBSP is particularly valuable in scenarios where multiple CBR systems exhibit identical precision yet differ in actual performance. The proposed method quantifies the retrieval strength, while normal precision captures the density of relevant retrieved materials. To validate the effectiveness and significance of the proposed metric, multiple use cases are designed, enabling a comprehensive comparison with benchmark metric commonly employed at this scale. As a case study, we utilize the REWIND forgery video dataset to retrieve manipulated videos corresponding to an original query. Experimental results demonstrate that the proposed metric provides a more accurate and reliable evaluation of CBR systems, particularly when considering the top-k retrieved media.