Our findings from evaluating the top-5 annotation accuracy across all models (figure ) suggest a uniform performance. The absence of MAGMa substructure labeling had only a minimal impact on retrieval accuracy, whereas a somewhat more notable reduction in annotation accuracy can be observed when the fingerprint unfolding and neutral loss featurization components are removed. This consistent performance pattern persisted in the evaluation of top-20 annotation accuracy, revealing negligible differences between the models (figure ).