There are no conflicts to declare. Extended Data Tab. 2 | Data-efficiency comparison of best-performing GPT-3-based approaches with best-performing baselines. For the best comparison, we also split into (pre-trained) deep-learning (DL)based baselines (here, MolCLR, ModNet, CrabNet, and TabPFN 61 ) and baselines not using (pretrained) deep-learning approaches (n-Gram, Gaussian Process Regression, XGBoost, random forests, automated machine learning optimized for materials science ) on hand-tuned feature sets. There are several caveats to this analysis. First, focusing on the low-data regime might not always be the most relevant perspective. Second, we only focus on the binary classification setting in this table. Third, we focus on the F 1 macro score for this table (all cases are class-balanced). Fourth, we consider the performance of the GPT-3 model for ten training data points as a reference. We provide more details in Supplementary Note 7. The version of GPT-3 we utilized in this work has been trained on data up to Oct 2019 that mostly comes from web scraping (Common Crawl 62 and WebText ) along with books corpora and Wikipedia. Structured datasets, however, have not been part of the training. Also, note that our approach works well on representations that have not been used for the original datasets (e.g., SELFIES, InChI