Abstract: Malaria is one of the most significant public health concerns in many tropical and subtropical regions of the world, with 40% of the world population exposed to malaria-causing parasites. Increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays there is a pressing need to identify and develop new drug-based antimalarial therapies. In an effort to overcome this problem, the main aim of this study was to develop simple linear discriminant-based QSAR models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense a database of 1562 organic-chemicals having great structural variability; 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool not only for theoretical chemist but also for other researchers in this area. These series of compounds were processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived toward discrimination between antimalarial and non-antimalarial compounds. The models (including non-stochastic and stochastic indices) classify correctly more than 93% of compounds in both training and external prediction datasets. They showed high Matthews´ correlation coefficients; 0.889 and 0.866 for training and 0.855 and 0.857 for test set. Models predictivity were also assessed and validated by the random removal of 10% of the compounds to form a test set, for which predictions were made from the models. The overall mean of the correct classification for this process (leave-group 10% full-out cross-validation) for obtained equations with non-stochastic and stochastic quadratic fingerprints were 93.93% and 92.77%, correspondingly. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The models developed with non-stochastic and stochastic quadratic indices were then used in a simulation of a virtual search for Ras FTase inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously un-known antimalarials compounds. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two Malaria strains, using Chloroquine as reference, was performed. An accuracy of 100% with the theoretical predictions was observed. The compound 3 shown antimalarial activity, being the first report of an arylaminomethylenemalonate having such activity. This result opens a door to a virtual study considering a higher variability of the central core already evaluated, as well as other chemicals not included in this family. We conclude that the approach described here seems to be a promising QSAR tool for molecular discovery of novel classes of antimalarial drugs which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illness.