However, the priority generally lies in avoiding false positives. is necessary to reject the null hypothesis that the enrichment scores observed in the tested models could have come from a random classifier. By implementing the Bonferroni correction in our analysis, we can more rigorously evaluate the statistical significance of the observed enrichments. This safeguards against attributing apparent improvements in docking performance to the mere artifact of having tested multiple docking models. Our findings substantiate that the performance superiority of DockOpt over the previously published DUDE-Z parameterizations remains statistically significant even after adjusting for multiple comparisons, thereby offering a reliable metric for assessing the efficacy of our method.