To gauge the relative performance and contributions of the various scoring functions, we performed a similar analysis, which is shown in Figure . On the DEKOIS dataset, the top performers were KORP-PL, RTMScore and CNN-Score, showing significant presence in the top 0.01% the dataset (62, 62, and 61 counts respectively). In contrast, we found NNScore, PLECscore, LinF9, and CNN-Affinity to be relatively poor performers. On DUD-E, KORP-PL and RFScoreVS were the highest-performing scoring functions, closely followed by CNN-Score. This is not surprising concerning RFScoreVS as DUD-E was used as the training set for this scoring function and higher relative performance is therefore expected. With that in mind, KORP-PL, and CNN-Score still showed good performance. As we found for the DEKOIS dataset, NNScore, PLECscore, LinF9, and CNN-Affinity all performed poorly on DUD-E. On Lit-PCBA, RFScoreVS, CNN-Score, KORP-PL and ConvexPLR showed the best performance although most of the scoring functions were fairly closely ranked. From this analysis, we can conclude that KORP-PL, CNN-Score, RTMScore and RFScoreVS showed the highest overall performance across all three datasets while CNN-Affinity, NNScore and PLECScore showed markedly lower performance. Additionally, it is clear that no single scoring function consistently outperforms the rest, which is expected as scoring functions can be highly target-specific. Additionally, we investigated how the number of scoring functions taken into account during the consensus affects the enrichment performance (Figure ). For all studied datasets, a Gaussian distribution was observed showing that a consensus of five to seven scoring functions was significantly more represented in the top 0.01% and 0.1% for each of the datasets we studied. These data indicate that including too many scoring functions in the consensus can decrease enrichment performance due to increased noise and disagreement. Given the substantial computational cost, we recommend using more than five scoring functions in DockM8 only if proven beneficial for the specific target.