• Consensus: The Zscore_best and RbR_best methods showed superior performance in all of our test cases, making them a good starting point for a prospective VS campaign. • Pose selection: KORP-PL performed consistently well on all datasets, likely providing a reliable starting point for VS on new targets. • Docking selection: While we did not specifically compare docking programs, we can derive some data from the bestpose_* methods as these take the pose with the best score from the selected docking program. GNINA offers the best performance at the expense of higher computation time. PLANTS is recommended if computation time is crucial, as its performance can be improved with suitable consensus and scoring methods. • Scoring functions: For a VS campaign, we recommend using three to six scoring functions for optimal balance between performance and computational cost. The scoring functions KORP-PL, ConvexPLR and CNN-Score provide good performance at reasonable computational costs. RTMScore or RFScoreVS can enhance performance but significantly increase computation time. Whenever available, known wellperforming scoring functions on the target at hand should be included in the consensus.