To benchmark our similarity search performance, we compare to the traditional method of encoding molecules using Morgan fingerprints and computing similarities using a Tanimoto distance. As an illustrative example, we show a query molecule (Figure ) from the Enamine HTS Collection in property space and the property distributions of its 100 nearest neighbors computed via ChemGPT embeddings (Figure , red contour plots) and the fingerprint method (Figure , blue contour plots). Again, ChemGPT is not fine-tuned on this dataset, the pre-trained model is able to generate "chemically meaningful" embeddings for Enamine HTS, which is a standard chemical library for high throughput screening. The ChemGPT embeddings are dense, real-valued, high-dimensional vectors, so a combination of dimensionality reduction and flexible choice of distance metric (L1, L2, cosine, etc.) yields a smooth and tunable distribution of nearest neighbors in property space. In this example, we use PCA to reduce the embedding dimension from 1024 to 100 and compute distances with a cosine similarity.