The Aedes aegypti mosquito belongs to the order Diptera and is one of the main vectors of transmission of etiological agents that cause several diseases. This mosquito can transmit diseases such as dengue, yellow fever, Zika, chikungunya, among others. The aim of this study was combining structure-based and ligand-based virtual screening (VS) techniques to select potentially larvicidal active molecules against Ae. aegypti from in-house secondary metabolite dataset (SistematX). From the ChEMBL database, we selected a set of 161 chemical structures with larvicidal activity against Ae. aegypti to create random forest models with an accuracy value higher than 82% for cross-validation and test sets. Afterward, the ligand-based virtual screen selected 38 secondary metabolites . In addition, a structure-based virtual screening was also performed for the 38 molecules selected. Finally, using consensus analyzes approach combining ligand-based and structure-based VS, five molecules were selected as potential larvicidal against Ae. aegypti.