SpaceHASTEN has some similarities to the previously described HIDDEN GEM method, where 2D-chemical similarity was used to guide docking of ultra-large enumerated chemical spaces . However, SpaceHASTEN has several crucial improvements over HIDDEN GEM. Firstly, and most importantly, SpaceHASTEN uses non-enumerated virtual libraries as the screening input, instead of the enumerated ones. Secondly, SpaceHASTEN uses machine learning to predict the docking scores of the compounds retrieved from the nonenumerated chemical space, whereas HIDDEN GEM relies only on chemical similarity to pick out the compounds with a high docking score. Finally, SpaceHASTEN has an easy-touse graphical user interface ready to be used in an industrial setting.