Next, we evaluated the MIST database retrieval performance, including a comparison with the benchmark ) Ablation results for MIST without MAGMa substructure prediction, without simulated data, without neutral loss featurization (pairwise), and without fingerprint unfolding. Each model was trained five times with different random seeds. standard, CSI:FingerID. Initial comparisons among the public and retrained MIST versions reveal that their performances are highly similar (figure ), indicating consistent spectrum annotation capabilities across versions. Specifically, when utilizing predicted fingerprints for database retrieval using the cosine distance, the pretrained MIST model achieves a top-1 annotation accuracy of 8.9 %, in contrast to 11.0 % for the retrained model. When employing contrastive embeddings for retrieval, both models exhibit identical top-1 annotation accuracy of 10.0 %.