As the MF-PCBA collection is based entirely on real-world data generated by high-throughput screening experiments, it enables the design and evaluation of new machine learning architectures specifically for drug discovery. Additionally, MF-PCBA can be used as a suite of benchmarks for evaluating the performance of newly-developed machine learning applications. For example, MF-PCBA can support the rapidly developing field of representation learning with graph neural networks, particularly in terms of graph-level regression tasks, which are currently underrepresented. At the same time, the collection offers a unique challenge in the form of multi-fidelity (multi-modality) integration. This is a characteristic of drug discovery by HTS that presents new opportunities for computational modelling and ultimately for improved experimental and hybrid wet-lab and in silico workflows. We also envision that the data curated here can be used for other modern machine learning techniques such as generative approaches and few-shot learning.