The performance of artificial neural network (ANN) in predicting the affinity of a series of 65 new azapirone derivatives for rat brain serotonin 5-HT1A receptors based on high-performance liquid chromatography (HPLC) retention data and on non-empirical structural parameters of the compounds was studied. Affinity of the agents for rat brain 5-HT1A receptors were assessed in vitro and expressed as inhibitor constant values, Ki. The retention parameters determined in 14 HPLC systems along with compounds structural descriptors from calculation chemistry were considered in ANN analysis. Supervised method of ANN learning with back-propagation strategy was used in ANN calculations. Two models of ANN of similar architecture were designed: the first one for the data based on chromatographic retention data and the second based on structural parameters of the agents. Each ANN model was trained with the data of training set. It was next used to classify the agents from the testing set into two groups: active (Ki ≤ 50 nM) and inactive compounds (Ki ≥ 50 nM). A high prediction performance of both ANN models considered as regards the affinity of new azapirone derivatives for the serotonin 5-HT1A receptors was demonstrated. However, the percent of correctly classified compounds was higher in the case of the ANN processing of the non-empirical structural descriptors of azapirone derivatives. Since the ANN analysis of the retention data and of the structural parameters originating from calculation chemistry allows to segregate drug candidates according to their pharmacological properties that, in consequence, may be of help to limit the number of biological assays in the search for new drugs.