Overall, five of the top six performing workflows combined physics-based and ML techniques. All five workflows used ML to score docked poses, and two (WF1454 and WF1456) used ML to accelerate screening within active learning cycles. A more conventional, purely physics-based approach (WF1448) also proved successful, demonstrating that well-established physics-based virtual screening techniques remain competitive when deployed by experienced computational chemists. While only 22% of the workflows (five out of 23) used in Round 1 explicitly accounted for protein flexibility using conformational ensembles (WF1419, WF1422, WF1447, WF1448) or flexible docking (WF1414) (Figure ), they represented 50% of the most successful workflows (three out of six) (Figure ). Considering the well-known conformational dynamics of helicases , including Nsp13 5 , accounting for receptor flexibility may indeed have increased chances of success.