In this review an overview of the application of computational approaches is given. Specifically, the uses of Quantitative Structure-Activity Relationship (QSAR) methods for in silico identification of new families of compounds as novel tyrosinase inhibitors are revised. Assembling, validation of models through prediction series, and virtual screening of external data sets are also shown, to prove the accuracy of the QSAR models obtained with the TOMOCOMD-CARDD (TOpological MOlecular COMputational Design Computer-Aided Rational Drug Design) software and Linear Discriminant Analysis (LDA) as statistical technique. Together with this, a database is collected for these QSAR studies, and could be considered a useful tool in future QSAR modeling of tyrosinase activity and for scientists that work in the field of this enzyme and its inhibitors. Finally, a translation to real world applications is shown by the use of QSAR models in the identification and posterior in-vitro evaluation of different families of compounds. Several different classes of compounds from various sources (natural and synthetic) were identified. Between them, we can find tetraketones, cycloartanes, ethylsteroids, lignans, dicoumarins and vanilloid derivatives. Finally, some considerations are discussed in order to improve the identification of novel drug-like compounds based on the use of QSAR-Ligand-Based Virtual Screening (LBVS).