This paper describes my submission to one of the sub-problems formulated for the Predictive Toxicology Challenge 2001. The challenge is to predict the carcinogenicity of chemicals based on structural information only. I have only tackled such predictions for bioassays involving male rats. As we currently do not know the true predictions for the test-set, all we can say is that one of the models supplied by us seems to be optimal over some subrange of the ROC spectrum. The successful model uses a voting approach based on most of the sets of structural features made available by various other contestants as well as the organizers in an earlier phase of the Challenge. The WEKA Machine Learning workbench served as the core learning utility. Based on a preliminary examination of our submission we conclude that reliable prediction of carcinogenicity is still a far away goal.