While the expansion of our NCB space with these out-of-sample structures showed that our model carries predictive power even with substrates it was not trained with, the model can be adapted over time through expansion of the training data, which would be recommended if a substituent chemically distinct from our current library is found to be relevant in NCB design. Nevertheless, our model tends to predict stabilities higher than the benchmark DFT calculations, especially for the three high-error out-of-sample structures. This error trend reduces the chance that a highly stable structure would fail to advance to an experimental stage in a high-throughput NCB discovery pipeline, and even though some "false positives" might appear in the top ranks, our results show that is unlikely. For example, if an experimental group chose to synthesize and characterize the top 10% of stability-ranked structures, including the out-of-sample designs, this would result in two of the fifteen candidates (rounded up), with DFT calculated ∆G values that would have excluded them from the top 10%, but that still place them in the top 20%.