Most of the recently published quantitative structure-property relationship (QSPR) models, which can be used to predict environmentally relevant physicochemical data for persistent organic pollutants (e.g., polychlorinated dibenzo- p-dioxins, dibenzofurans, and biphenyls), employ molecular descriptors obtained by means of relatively costly calculations at the density functional theory (DFT) level. However, new semiempirical methods, PM6 and RM1, have recently been developed by J. J. P. Stewart's group. In this study, we compared various QSPR models based on DFT (B3LYP functional) descriptors with the same models based on semiempirical (PM6 and RM1) descriptors. We recalibrated 10 previously published models (for different properties and groups of congeneric compounds) employing PM6 and RM1 descriptors instead of B3LYP ones. We demonstrated that by applying RM1 and PM6 descriptors, we could obtain QSPR models with quality similar to that of models based on B3LYP descriptors. This level of accuracy was out of reach for the models employing AM1- and PM3-based descriptors.