The top I rank score contributed by our team was based on a consensus of 28 models. The consensus modeling allows leveraging performance of individual models by decreasing their variances and biases. The same idea is the basis of ensemble methods, such as Random Forest (RF), which uses bagging, or Associative Neural Networks (ASNN), which uses random split of data, in which an average of models developed using the same approach is used. It is also used in boosting approaches, such as CatBoost and XGBoost which focuses on fitting more hard to learn samples. Based on our successful experience with using consensus models in ToxCast and Tox21 49 challenges our strategy was to use the same approach with the best set of descriptor and machine learning methods available in openOCHEM, which resulted in the Top I rank winning solution. This result highlights an importance of consensus modeling as well as shows the high potential of the OCHEM platform to contribute best accuracy models.