This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction challenge—the first CASP competition to incorporate targets from pharmaceutical discovery projects. We have assembled and characterized protein-ligand complexes for four proteins that are known or candidate drug targets: human chymase, human cathepsin G, human autotaxin, and the SARS-CoV-2 main protease. The collection encompasses over 200 co-crystal structures at resolutions better than 2.7 Å, paired with binding measurements for approximately 160 compounds covering a broad affinity range (nanomolar to high micromolar). Many systems feature potentially challenging characteristics, including chymase’s electropositive surface and acidic ligands, which require proper handling of titratable ligand groups; autotaxin complexes with and without zinc coordination; and a SARS-CoV-2 protease crystal form exhibiting an unusually open active site conformation. We describe the experimental approaches—from protein production and crystallization to binding assay development—that yielded these reference data. Contributed by scientists at Hoffman LaRoche and Idorsia Pharmaceuticals, these datasets represent actual drug discovery projects and therefore represent a realistic testbed for assessing how computational methods perform on pharmaceutically relevant targets.