Training performance estimation and neural scaling enable significant improvements to model performance, and computational and data efficiency. Next, we briefly highlight the practical outcomes and usages of TPE and neural scaling as enabling technologies for scalable scientific deep learning. Based on the results presented above, TPE can be used in conjunction with any HPO routine to enable aggressive early stopping and accelerate HPO without sacrificing model performance. Clearly, the benefits of this approach become more pronounced in chemical and biological applications, where new network architectures must be continuously retrained, optimized, and evaluated on heterogeneous datasets.