Classically, SBVS is performed using physics-based methods such as docking in which the 3D coordinates of a hypothetical ligand and the target are first identified and then scored according to an empirical scoring-function. The ligand placement is a strength of docking methods, and in many cases, they can retrieve molecules in correct binding poses. The more problematic aspect is the ranking or scoring of the ligands and poses based on predicted binding affinity. A recent data driven approach based on diffusion generative modeling outperformed docking at identifying bound poses in a blind docking task. As well as ligand positioning and scoring, the study also required binding site identification, a problem for which docking is not intended and for which other methods are better suited . This may partly account for the worse performance of docking in that study, but the 'all-in-one' diffusion model was an impressive solution compared to protocols of multistep and multimethod physics-based approaches. Thus, the key ligand scoring step in docking is known to underperform compared to placement and suffers from a lack of sampling and the underlying oversimplicity of docking scoring-functions. More powerful and expensive physics-based ensemble approaches such as free energy perturbation can be used for this task. However, these methods have a high computational cost, technical difficulty, and sensitivity to input setup, that hampers large scale virtual screening and is the subject of active research. The breakthroughs in machine learning (ML) and deep learning (DL) approaches coupled with the increasing experimental structures and bioactivity data have led to the emergence of 3D ML and DL models as an alternative to docking scoring functions. The 3D coordinates of protein- ligand complexes are used as input but the aims for specific models can be varied: predict binding poses, separate binders from non-binders, rank based on activity or predict activity. The goals are attractive not only because of the value for drug discovery, but also the need for improvement versus docking, particularly in high throughput applications. There are various caveats for most reported ML/DL affinity prediction models that are usually true for docking. They arise from the simplification of ligand partitioning from solvent to protein-bound phases and include the lack of an ensemble approach, the absence of (de)solvation terms, the simplified or absence of entropic considerations, etc. Learning affinity from single experimental 3D protein-ligand complexes emphasizes the interaction potential between protein and ligand and only indirectly captures entropy or (de)solvation factors.