Experimental and computational advances paved the way to increasingly large, diverse, and challenging datasets. For example, although QM9 is one of the most well-known quantum mechanics benchmarks, concerns regarding its size (around 130,000 compounds) and composition (only 5 atom types and a maximum of 9 heavy atoms per molecule) led to projects such as Alchemy (up to 14 heavy atoms), PubChemQC (quantum properties for up to 3 million molecules), QMugs (over 665,000 diverse molecules, up to 100 heavy atoms per molecule, and quantum properties at multiple levels of theory), and ANI-1x and ANI-1ccx (quantum properties at different levels of theory for 5 million, respectively 500,000 molecules). For drug discovery, the well-known PCBA dataset is a collection of high-quality dose response data, formulated as a multi-task learning benchmark from 128 high throughput screening assays. Models trained on experimental data of this nature are often used in a virtual screening setting, with the goal of identifying novel compounds of therapeutic interest. For the specific task of virtual screening, an alternative benchmark named LIT-PCBA was developed , addressing limitations of existing datasets such as hidden molecular biases.