For a series of thiocarbamates, non-nucleoside HIV-1 reverse transcriptase inhibitors, few descriptors have been selected from a large pool of theoretical molecular descriptors by means of the ant colony optimization (ACO) feature selection method. The selected descriptors were correlated with the bioactivities of the molecules using the well known multiple linear regression (MLR) and partial least squares (PLS) regression techniques, and, to account for nonlinearity, also PLS coupled to radial basis function (RBF) on the one hand and radial basis function neural network (RBFNN) on the other. In this case study, the RBF/PLS results were better than those from the other modeling techniques applied. The prediction ability of the ACO/RBF/PLS-based quantitative structure–activity relationship (QSAR) model was found to be significantly superior to comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models previously established for this series of compounds. It was also demonstrated that RBF as a nonlinear approach is useful in deriving simple and predictive QSAR models, without the need to recourse to expeditious 3D methodologies.