HTVS campaigns often face constraints on the number of compounds that can be practically nominated for experimental testing. For instance, a model may identify 5000 compounds as active, yet operational constraints often cap experimental testing at 128 compounds, corresponding to the throughput of a single qHTS plate in 1536 well format with 11 concentration points per compound. This necessitates a caveat in the model's objective to nominate a top set of N compounds with a minimal false positive rate , which can also be described as a model with high early enrichment . In such scenarios, a model that identifies a smaller number of actives but ensures that such actives are included in the top (small N) nominations (e.g., the top 128) is as valuable as the one that can perfectly discriminate between actives and inactives but in a larger set. Conversely, a model that identifies 99% of all known actives in a large external set but includes none in the top 128 is ineffective for HTVS restricted by the size and number of plates used in the experimental screening. These considerations lead to an expressed need to redefine the performance metrics, where the number of actives within the top nominations of a fixed size emerges as a key indicator of a model's utility in HTVS.