In this work, we expand upon our initially-reported collection of public multi-fidelity HTS datasets, reporting a total of 60 datasets with over 16.6 million unique protein-molecule interactions extracted from PubChem. We cover in detail the search, selection, and filtering steps, as well as providing a performance evaluation based on the methodology proposed by Buterez et al . We envision that the provided datasets will motivate the development of new graph representation learning methods capable of multi-fidelity modelling and transfer learning, thus advancing the state-of-the-art in computational drug discovery projects. At the same time, we believe that the challenging properties of multi-fidelity HTS data will provide a new benchmark for evaluating machine learning algorithms, particularly in terms of molecule-level regression tasks, which are not well represented at this scale in existing work. It is important to note that the MF-PCBA datasets are representation agnostic, as the molecular information is presented in the form of SMILES strings, such that a variety of machine learning techniques are applicable (not exclusively deep learning). Ultimately, we hope that the MF-PCBA datasets will motivate further research and competition, thus advancing the field and enabling new possibilities for drug discovery and molecular modelling. The datasets can easily be assembled using the provided scripts ().