Tetrahydroimidazo[4,5,1jk][1,4]benzodiazepines (TIBO), as non-nucleoside analogues, constitute potent inhibitors of HIV-1 reverse transcriptase. In the present study, classification structure-activity relationship (SAR) models are developed to distinguish between high and low anti-HIV-1 inhibitors in this class of compounds. Different classifiers, such as support vector machines, artificial neural networks, random forests and decision trees have been established by using ten molecular descriptors. All models were validated using several strategies: internal validation, Y-randomization, and external validation. The correct classification rate ranges from 97% to 100% and from 70% to 90% for the training and test sets, respectively. A comparison between all methods was done in order to evaluate their performances. The contribution of each descriptor was evaluated to understand the forces governing the activity of this class of compounds.