Physics-based docking methods have been at the forefront of structure-based protein-ligand interaction prediction and virtual screening (VS) for decades. Most of these approaches consist of two main components: an algorithm for pose sampling and a scoring function for pose evaluation. Physics-based docking approaches primarily rely on molecular mechanics force fields, but they also utilize information derived from protein structural data, particularly, X-ray crystallographic data on protein-ligand complexes, and measured ligand binding affinities. 1 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 begun to explore ML-based 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. Most recently, a benchmark study on ML-based docking approaches was reported, further highlighting the potential of ML-based docking approaches in VS applications. In this work, we thoroughly assess the VS performance and pose sampling performance of a VS approach integrating the DiffDock-L with established scoring functions. Given concerns about the quality of the poses sampled by ML-based docking methods for redocking tasks, we study the validity and plausibility of the ML-based docking poses and compare them to those generated by physics-based docking methods.