For decades, classical docking approaches have been at the forefront of structure-based protein-ligand interaction prediction and virtual screening (VS). Most of these approaches consist of two main components: an algorithm for pose sampling and a scoring function for pose evaluation. Classical docking approaches primarily rely on molecular mechanics force fields but also use information derived from protein structural data (in particular, X-ray crystallographic data on protein-ligand complexes) and measured ligand binding affinities. 1 Recently, the development of machine learning (ML)-based docking approaches as an alternative to force field-based methods has gained traction. ML-based methods sample ligand poses using regression-based modeling (examples include EquiBind 5 , TANKBind, 6 E3Bind 7 , and KarmaDock 8 ) or generative modeling (examples include DiffDock and its successor, DiffDock-L, as well as SurfDock and FlowDock ). Benchmark studies indicate that ML-based approaches can predict the orientation and conformation of protein-bound ligands. However, some critique has been voiced about conceptual limitations of benchmark studies that may result in overestimating pose prediction performance. More recent works have started exploring MLbased docking approaches for VS applications. For example, the developers of SurfDock successfully employed their method to identify novel small-molecule inhibitors of aldehyde dehydrogenase 1 family member B1 (ALDH1B1). Likewise, the developers of KarmaDock identified leukocyte tyrosine kinase (LTK) inhibitors with their docking approach. In this work, we present a VS approach that integrates ML-based docking with established scoring functions (Figure ). We selected DiffDock-L, the latest version of DiffDock, as a representative deep learning (DL)-based pose prediction approach, as it has consistently achieved top performance among ML-based docking programs in terms of pose prediction accuracy, 10 physicochemical plausibility, and protein-ligand interaction recovery rate. DiffDock-L reports a confidence score for each generated docking pose that quantifies the likelihood of a pose having an RMSD of less than 2.0 Å to the hypothetical measured binding pose. This confidence score is ligand-specific and can only be used to rank poses of the same ligand.