Chagas' disease, which occurs particularly in South America is a human tropical parasitic disease, caused by Trypanosoma cruzi. A virtual screening in an in-house databank (SISTEMATX), of 469 Apocynaceae indole alkaloids, using models developed with fragment descriptors using Support Vector Machines (SVM) and Decision Trees (DT) were performed. A dataset 545 agrochemicals selected from ChEMBL database was used to generate both models and the prediction performance was tested using a small set of 44 alkaloids with the antitrypanosomal activity. From 469 Apocynaceae alkaloids, the SVM model selected, as actives, 5 similar alkaloids, from 2 species of the Aspidosperma genus (excelsum, marcgravianum), and the DT model selected 3 alkaloids from 3 species (gilbertii, nigracans, and subincanum) of the same genera from the SISTEMATX database. The values of Moriguchi octanol-water partition coefficient for these structures are between 2.3 to 5.3, and 5 alkaloids, passed the Lipinski alert index filter and Drug Like Score consensus (> 0.7), which indicate that these compounds are good candidates to become a drug. These structures might be an interesting starting point for antitrypanosomal studies. The methodology, applying fragment descriptors and machine learning, was rapid and can be applied for virtual screening for bigger databases.