Conducting HPO training on the same SMILES training testing split can easily lead to overfitting, as it optimizes the model within the same chemical space. An automatic stopping criterion is recently imposed to avoid overfitting in the HPO process through triggering termination when the objective function value is sufficiently close to the global optimum. The control mechanism is based on a high-probability confidence bound on the regret and a termination threshold, managing the optimization error through a computable upper bound of the simple regret. Specifically, termination is initiated when the maximum plausible improvement falls below the standard deviation of a cross-validation-based estimator.