Performance of DockM8: Our assessment of DockM8 across diverse datasets-DEKOIS 2.0, DUD-E, and Lit-PCBA-shows its superior performance in virtual screening (VS). DockM8-max consistently outperforms most other methods in enrichment factor and ranks among the best for AUC-ROC and BEDROC metrics (see Supporting Information). Unlike other methods that perform poorly on several targets, DockM8-max demonstrates excellent generalizability, never falling below 10.3% enrichment at 1% in DEKOIS and 21% in DUD-E. Even advanced methods like IGN , KarmaDock , SIEVE-Score , RTMScore , and CompScore lack this level of consistency. DockM8's flexibility allows tuning for each target, provided data on the target and its known binders is available. To address data limitations, DockM8 can generate decoys based on known active compounds, offering an optimization pathway even with limited data. Impact of structure resolution: Both DockM8-max and DockM8-best demonstrated exceptional performance on the Lit-PCBA dataset, with DockM8-max's EF1% consistently above 5.15%. While our decision to use the highest resolution available in this dataset may impact these results, this strategy aligns with SBVS best practices. As Tran-Nguyen et al. observed, scoring performance can indeed vary based on the crystal structure used . We are confident in our workflow's inherent strengths beyond structure resolution and aim to further validate its robustness across various structural resolutions in future studies.