Hyperparameter optimization is a key step when building ML models that can lead to significant gains in model performance. Given the sheer size of the search space with deep learning models, this step can often be operationally challenging. Chemprop provides a command line utility, chemprop_hyperopt, that automates this process by removing the need to manually define the search space of hyperparameters. Users can simply supply a list of keywords from which to build a hyperparameter search space (Table ). The number of trials of hyperparameter combinations to be tested can be set using the --num_iters argument. By default, the search space will first be randomly sampled for num_iters/2 trials before switching to targeted sampling via the tree-structured Parzen estimator algorithm for the remaining trials. The number of random trials to be used can be changed by setting --startup_random_iters to a value less than num_iters.