In standard AL workflows, models provide a predicted label and a measure of its uncertainty quantification (UQ). These outputs are used in various acquisition strategies, such as greedy and least-confidence sampling, to determine the examples to include in the next AL iteration. The performance of UQ methods in molecular property prediction strongly depends on the dataset itself. By default, Autoparty trains a committee of individual models. The variance across predictions from individual committee members defines the degree of prediction uncertainty. The default model trained during a hit picking party consists of three unique models that make up an ensemble. Each member is a deep neural network consisting of an input layer and two hidden layers of 1024 neurons. The number of unique possible grades determined by the user defines the output layer size. Each ensemble member learns from initial unique training data, sampled with replacement from the entire set of available graded molecules. This approach provides data augmentation through sampling while limiting overhead, as the training time for each component model remains short given the dataset size. Additionally, ensemble UQ provided high accuracy and good calibration in our testing (Supporting Fig. ).