We evaluated the performance of DockM8 on a subset of the challenging Lit-PCBA dataset (seven targets, Figure ). Lit-PCBA was designed to be an unbiased dataset for the evaluation of docking protocols and scoring functions, is based on real bioassay data, and aims to mimic experimental screening conditions. As above, a variety of other methods collected from the literature were included in the comparison (for all associated data, see the Supporting Information). In this experiment, DockM8 outperforms all other stateof-the-art methods in the literature. The median EF 1% for both DockM8-max with 7.83% and DockM8-best with 3.93% surpassed those of all other models for which enrichment factor data was accessible. Consistent with our findings on the previous two benchmarking datasets, The superior behavior of DockM8-max in generalizability is even more pronounced in this study, exemplified by its lowest enrichment rate of 5.15% for the mtorc1 target, which lies even above the median EF 1% value for all other methods. This further underscores the ability of the DockM8 workflow to be adapted to the target under consideration. These observations are also supported by the AUC-ROC and BEDROC metrics for which DockM8-max achieves median values of 0.623 and 0.107 respectively (see Supporting Information). The five best (on average according to EF1%) DockM8 methods are shown in Table .