jats:titleAbstract</jats:title> jats:sec jats:titleBackground</jats:title> jats:pThe assessment of molecular similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an experimental method for similarity assessment based on dynamic combinatorial chemistry.</jats:p> </jats:sec> jats:sec jats:titleResults</jats:title> jats:pIn order to assess molecular similarity directly in solution, a dynamic molecular network was used in a two-step process. First, a clustering analysis was employed to determine the network’s innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic molecular network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related molecules. After training, it was also able to classify unknown molecules based on the presence or absence of an ethylamine group.</jats:p> </jats:sec> jats:sec jats:titleConclusions</jats:title> jats:pThis is the first step in the development of molecular networks capable of predicting bioactivity based on an assessment of molecular similarity.</jats:p> </jats:sec>