We identified a lack of machine learning benchmarks that accurately reflect the practical aspects of drug discovery based on high-throughput screening (HTS), thus also limiting the potential for innovation in early-stage drug discovery projects. HTS follows a multi-tiered approach consisting of successive screens of drastically varying size and fidelity, most commonly a low-fidelity primary screen consisting of up to 2 million molecules in industrial settings, and a high-fidelity confirmatory screen of up to 10,000 compounds. Usually, the screens are used as successive filters to select the most promising molecules for further optimisation. Despite the success of HTS in identifying clinical candidates and FDA-approved drugs [Per10; Mac+11; BB18], computational methods have traditionally neglected the multi-tiered design of HTS. Existing datasets and applications, such as PCBA, LIT-PCBA, and approaches based on HTS fingerprints, are formulated on single-fidelity data [Ram+15; TNJR20; Pet+12; Hel+16; Lau+19; Stu+19]. The single-fidelity measurements generally correspond to the highest-fidelity measurements available (dose response), which leads either to extremely small datasets for machine learning (< 10, 000 compounds), or to highly-sparse representations extracted from hundreds of assays.