Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its conventional experimental test takes around three years to design, conduct, and interpret as well as the costs of hundreds of millions of dollars, millions of skilled personnel hours, and several animal lives. Both academia and private companies are actively trying to develop alternative methods, such as QSAR models. This paper reports a QSAR study for predicting carcinogenic potency of nitrocompounds bioassayed in female rats. Several different theoretical molecular descriptors, calculated only on the basis of knowledge of the molecular structure and an efficient variable selection procedure, such as Genetic Algorithm, led to models with satisfactory predictive ability. But the best-final QSAR model is based on the GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors capturing a reasonable interpretation. In fact, structural features such as molecular shape-linear, branched, cyclic, and polycyclic--and bond length are some of the key factors flagging the carcinogenicity of this set of nitrocompounds. This QSAR model, after removal of one identified nitrocompound outlier, is able to explain around 86% of the variance in the experimental activity and manifest good predictive ability as indicated by the higher q(2)s of cross- and external-validations, which demonstrate the practical value of the final QSAR model for screening and priority testing. This model can be applied to nitrochemicals different from the studied nitrocompounds (even those not yet synthesized) as it is based on theoretical molecular descriptors that might be easily and rapidly calculated.