In order to fully use the power of different approaches, we built a consensus model based on all models. Since performance of all methods were very similar, we used a simple average of model predictions. While the final challenge model (#1, Table ) had larger RMSE than the best descriptor based model developed with DNN using AlogPS+OEstate descriptors (#3, Table ), it had higher QWKs for both Leaderboard and private sets and contributed a winning top I model of the challenge. After the end of the challenge we noticed that by mistake the CatBoost model based on EFG descriptors was not included in the consensus. Its inclusion slightly increased QWK scores for both sets (#2, Table ).