Currently, ML methodologies continue to open new possibilities for generating hundreds and thousands of new molecules from an exhaustive search in chemical space. To perform the search in the chemical space faster and more efficiently, in particular for large data sets, the visualization methods should scale well with the number of molecules ("haystack size"); find the most relevant compounds (e.g., find the "needle," irrespective of the size of the haystack); and be affordable to run on standard hardware.