Two groups of datasets are used. A curated set of 25 publicly available datasets is utilized for false positive detection and active learning, primarily based on the MF-PCBA benchmark. All 25 datasets consist of a primary high throughput screen and a confirmatory screen on a subsection of the primary actives (Table -3). As some data valuation functions need a separate validation set to calculate influence values, ten percent with the highest primary screen PubChem activity scores were used as a separate validation set. To benchmark importance-driven undersampling, a subset of the MolData benchmark, a group of 10 datasets associated with the disease aging, was used (Table ). Here, the train-test-val splits suggested by the MolData paper were kept. For all datasets, the SMILES strings were imported from the PubChem datasets, sanitized, and standardized into their canonical form.