Applications of generative artificial intelligence (AI) methods that propose drug-like ligands frequently assess the quality of their generated ligands in terms of the quantitative estimate of drug-likeness (QED) , on a scale of 0 to 1. The distribution of QED values for the compounds in BindingDB peaks at about 0.5, for compounds drawn from both patents and articles. This result is similar to that initially reported for an early version of ChEMBL 46 . Generative AI methods also frequently use measures of synthetic accessibility (SA) to guide the generation of compounds that are amenable to chemical synthesis. As shown in Figure , a metric of synthetic accessibility available in the 3/2023 release of RDKit 47,48 peaks at about 3, on a 0 to 10 scale, for all compounds in BindingDB, indicating non-trivial synthetic challenges for most compounds. Interestingly, compounds in patents are scored as slightly more synthetically accessible, on average, than compounds in articles.