The uncertain strategy prioritizes selection of the predictions for which the model showed largest uncertainty. Here, we model this uncertainty as standard deviation in predictions of 5 ensemble models trained on the same data with different random starting weights. Similar to random selection, this selection strategy places no priority of finding strong binders, yet both the rate of their discovery and the accuracy of their predicted free energies is worse than random selection of ligands.