The overall SpaceHASTEN process is shown in Figure . The virtual screening campaign starts with acquiring a set of random molecules (seeds) from the chemical space to be screened. One round of screening consists of training a new machine learning model version, followed by three similarity searching cycles (the first cycle is run using the docked scores to pick the query molecules, followed by two cycles where the predicted docking scores from the previous cycles are used to pick the query molecules for the next cycles) and finally, ending the round by running one docking iteration. Two rounds of SpaceHASTEN are needed for a complete virtual screening campaign. The process, with the notable exception of the machine learning training, is trivial to parallelize and thus can be sped up by increasing the number of cores used in the computation. The chemical searching cycle is repeated two times using the top 1000 predicted compounds as queries. Finally, the best predicted compounds are docked in iteration and the virtual hits are exported for the user for additional processing.