The avg methods consistently performed worse across all three datasets compared to their best equivalents, indicating that including information from multiple poses may not benefit virtual screening performance (Figure ). Specifically, Zscore_best performed best on the DUD-E and DEKOIS datasets, while RbR_best excelled on the challenging Lit-PCBA dataset. This variation might be due to differences in dataset size and chemical space or our use of subsets rather than complete datasets. This highlights the importance of customizing the VS approach to each target's characteristics. Our findings suggest that the choice of consensus method should be tailored to the specific demands of each target, recognizing that each dataset and target may influence the efficacy of different methods.