Retrospective benchmarking exercises are critical to compare predictive computational methods and carefully assembled datasets play a central role for example to evaluate docking, virtual screening or free energy perturbation methodologies . While the value of these resources is generally well appreciated among computational chemists and data scientists, one may be surprised to see new ML-driven virtual screening tools being published every month that perform better than "all others" when tested for example on the PoseBusters dataset . Skeptical data scientists may wonder whether data leaked between training, test and validation sets while seasoned drug-hunters and experimentalists may refer to the old Danish proverb saying that "It is difficult to make predictions, especially about the future".