Ligand-based screening of large molecular databases can help reduce costs with experiments by filtering and ranking promising compounds in an initial stage of the drug developing process. However, some ligand-based methods can be ineffective when presented with a high-dimensional number of attributes extracted from an extensive dataset of compounds. Herein, we propose a drug-mining algorithm that can be used to screen ligands and repurpose known drugs, from any dataset for any target. The Milk-Way algorithm combines mathematical and regression methods to select promising compounds from a high-dimensional dataset without the use of massive computational power. We carried out a prospective screening targeting cyclin-dependent kinase two (CDK2), an attractive target for therapeutics designed to arrest or recover control of the cell cycle. The combined use of the algorithm metrics and molecular docking suggested five promising drugs to be repositioned (Pramocaine, Prochlorperazine, Trifluoperazine, Methionine, and Pergolide), in which three were already mentioned as possible inhibitors of related diseases in the literature.