We have presented MF-PCBA, a new collection of 60 multi-fidelity molecular datasets that reflect the real-world nature of HTS projects in drug discovery. With over 16.6 million unique protein-molecule interactions in total, MF-PCBA represents one of the largest collections of graph-level regression datasets, with the possibility to easily adapt to classification. The multi-fidelity aspect, enabled by the inclusion of primary and confirmatory screening data, is underrepresented in existing molecular modelling efforts, most likely due to uncertainties regarding the noisiness of the data and the vastly different number of compounds between the primary and confirmatory screens. Here, we have further validated the value of utilising multi-fidelity bioactivity measurements, for example by observing uplifts in R 2 , on average, between 33% and 40% when using SD labels and depending on the algorithm, further supporting the need for an effective and public multi-fidelity benchmark, as provided here. Moreover, we have shown that while the SD/DR correlation is a factor in the level of benefit gained by using multi-fidelity modelling, it is not as strong a factor as had previously been thought. We showed some tentative signs that the roughness of the activity landscape, as measured by the ROGI, might be another factor, but further work is needed to clarify this and identify additional factors in similar or larger collections. As shown previously, multi-fidelity modelling can also lead to other desirable properties such as more selective models and new candidate selection workflows. Thus, we envision multi-fidelity data and models to be a natural step forward for early-stage drug discovery projects based on high-throughput screening. To support this vision, the 60 MF-PCBA datasets are available publicly, aiming to capture the heterogeneity of HTS campaigns, for example in terms of assay type, targets, screening technologies, primary and confirmatory screen sizes, concentrations, and scoring metrics. Moreover, due to the challenging nature of multi-fidelity integration, MF-PCBA can act as a representation learning benchmark for future machine learning research, and in particular for the rapidly advancing area of graph neural networks. At the same time, MF-PCBA is not limited to deep learning as a multitude of existing machine learning techniques can be applied, enabling more varied comparisons. Overall, we believe that the presented datasets can be used as a basis for advancing both computational drug discovery efforts and molecular machine learning techniques.