For the first time, energy-based neural networks (EBNNs) were applied to build structure-activity models. The Hopfield Networks (HNs) and the Restricted Boltzmann Machines (RBMs) were used to build one-class classification models for conducting similarity-based virtual screening. The AUC score for ROC curves and 1%-enrichment rates were compared for 20 targets taken from DUD repository. Five different scores were used to assess similarity between each the tested compounds and the training sets of active compounds: the mean and the maximum values of Tanimoto coefficients, the energy for HNs, the free energy and the reconstruction error for RBMs. The latter score was shown to provide the superior predictive performance. Additional advantages of using EBNNs for similarity-based virtual screening over the state-of-the-art similarity searching based on Tanimoto coefficients are: computational efficacy and scalability of prediction procedures, the ability to implicitly reweight structural features and consider their interactions, their “creativity” and compatibility with modern deep learning and artificial intelligence techniques.