Figure 1: Offline Model-Based Optimization (MBO): The goal of offline MBO is to optimize an unknown objective function with respect to, provided access to only as static, previously-collected dataset of designs. Machine learning methods have shown tremendous promise on prediction problems: predicting the efficacy of a drug, predicting how a protein will fold, or predicting the strength of a composite material. But can we use machine learning for design? Conventionally, such problems have been tackled with black-box optimization procedures that repeatedly query an objective function. For instance, if designing a drug, the algorithm will iteratively modify the drug, test it, then modify it again.