The present work is devoted to the development and application of a multi-agent Quantitative Structure-Activity Relationship (QSAR) classification system for tyrosinase inhibitor identification, in which the individual QSAR outputs are the inputs of a fusion approach based on the voting mechanism. The individual models are based on TOMOCOMD-CARDD (TOpological Molecular COM-putational Design-Computer Aided Rational Drug Design) atom-based bilinear descriptors and Linear Discriminant Analysis (LDA) on a novel enlarged, balanced database of 1,429 compounds within 701 greatly dissimilar molecules presenting anti-tyrosinase activity. A total of 21 adequate models are obtained taking into account the requirements of the Organization for Economic Cooperation and Development (OECD) principles for QSAR validation and present global accuracies (Q) above 84.50 and 79.27% in the training and test sets, respectively. The resulted fusion system is used for the in silico identification of synthesized coumarin derivatives as novel tyrosinase inhibitors. The 7-hydroxycoumarin (compound C07) shows potent activity for the inhibition of monophenolase activity of mushroom tyrosinase giving a value of inhibition percentage close to 100% in vitro assays, by means of spectrophotometric analysis. The current report could help to shed some clues in the identification of new chemicals that inhibit tyrosinase enzyme, for entering in the pipeline of drug discovery development.