A quintessential task in any molecular discovery campaign is identifying promising candidate molecules amidst an enormous chemical space . With the democratization of computing resources, computational oracles can be deployed to query larger chemical spaces in search of the desired property profile. The use of such oracles has enabled researchers to identify functional materials , therapeutics , and catalysts , thus accelerating chemical discovery. However, there is generally a trade-off between oracle predictive accuracy and inference cost, such that the computational budget imposes a pragmatic constraint. Provided a sufficiently sample efficient model, it is conceivable for wet-lab experiments to be the oracle itself, as enabled by a high-throughput experimentation platform. Correspondingly, designing computational workflows and algorithms that are performant under minimal oracle calls is widely beneficial to the field of molecular design.