First, all of the above studies report errors for sets of ligands binding to the same protein target. A more stringent test for ABFEs would be to score and rank protein -ligand pairs involving different proteins as well as different ligands. A mature ABFE pipeline should be capable of handling such a scenario. Plentiful structural and affinity data is available from the refined set of PDBBind 68,69 -a curated set of protein -ligand structures and affinities compiled from literature. Moreover, previous ABFE studies (as well as RBFE studies for that matter) suffer from a grave yet inconspicuous flaw -they use only binders to the protein target (positive data) but not nonbinders (negative data). This scenario is also at hand when training most artificial intelligence (AI) or ML models to predict protein -ligand affinity , yet this is completely unrealistic in drug discovery where the supermajority of ligands are decoys, i.e. do not actually bind the protein target. Thus, all ligands an ML or AI model encounters during training are binders, reinforcing the utterly incorrect supposition that every ligand the model encounters will likely bind the target, even if only weakly. Indeed, even in an RBFE scenario, where one is interested in promising analogs of a known binder, not all of the analogs generated by a medicinal chemist or AI are likely to be binders, yet all RBFE benchmarks published so far report results using only positive but not negative data . Not including negative data in ABFE and RBFE studies is likely to severely overestimate the performance of these methods. Therefore, to truly gauge CADD's capacity to compass the discovery of the next generation of antineoplastics, the field needs a rigorous benchmark of RBFEs, ABFEs, and their recalls in drug searches involving ample positive as well negative data. This will be the subject of a future manuscript.