Matrix metalloproteinases (MMPs) have distinctive roles in various physiological and pathological processes such as inflammatory diseases and cancer. This study explored the performance of eleven scoring functions (D-Score, G-Score, ChemScore, F-Score, PMF-Score, PoseScore, RankScore, DSX, and X-Score and scoring functions of AutoDock4.1 and AutoDockVina). Their performance was judged by calculation of their correlations to experimental binding affinities of 3D ligand-enzyme complexes of MMP family. Furthermore, they were evaluated for their ability in reranking virtual screening study results performed on a member of MMP family (MMP-12). Enrichment factor at different levels and receiver operating characteristics (ROC) curves were used to assess their performance. Finally, we have developed a PCA model from the best functions. Of the scoring functions evaluated, F-Score, DSX, and ChemScore were the best overall performers in prediction of MMPs-inhibitors binding affinities while ChemScore, Autodock, and DSX had the best discriminative power in virtual screening against the MMP-12 target. Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment. Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.