This study presents a framework that incorporates ML-based pose sampling using DiffDock-L and scoring with the established functions Vina and Gnina. Our findings demonstrate that MLbased pose sampling performs comparably to physics-based pose sampling methods in VS applications. Moreover, the choice of scoring function can be crucial to the success of virtual screening.