The difference is less dramatic when running multiple instances of Chemprop on each GPU during the prediction step: With parallelized Chemprop, for the large docking fraction-SurA model to consume less time than ten iterations of the model with the small docking fraction, it can only run for three iterations (Table ). In this case, its recalls of 81%, 72%, and 66% of the top-scoring 100, 1000, and 10 000 virtual hits are only slightly higher than with ten iterations of the smaller docking fraction model (compare also Figure ). For GAK, similarly, the model with the larger docking fraction would be faster when run for only 3-5 iterations with recalls being approximately on par or better by 5-15% (when stopped at iteration 3 or 5, respectively; see also Table and Figures and). We continued our run with the smaller docking fraction for GAK for a total of 25 iterations to study whether recalls would eventually converge. With multiple Chemprop instances per GPU, this is still almost two days faster than ten iterations of the large docking fraction model. Notably, on the final iteration, the recall of the top 100 virtual hits is lower by only 1%, and for the top 1000, by 5%, when only 3.9 million compounds were docked -less than during three iterations of the large fraction model.