The fingerprints predicted by MIST can be directly utilized for spectrum annotation, employing nearest neighbor searching to match the MS/MS-derived fingerprints against those extracted from molecular structures within chemical databases, such as PubChem. MIST also harnesses a metric learning approach, utilizing a contrastive model to transform both spectral and chemical fingerprints into embeddings, which are then subjected to nearest neighbor searching. Initial findings by Goldman et al. [2] indicate that the fingerprints predicted by MIST from MS/MS spectra surpass those generated by CSI:FingerID 5 in terms of accuracy. However, it is only with the application of contrastive fine-tuning that MIST demonstrates a superior performance in database retrieval tasks. To understand these results, this reusability report aims to critically assess the MIST reproducibility and evaluate its novel neural network architecture contributions. Specifically, we describe the extent to which the originally reported results can be replicated and apply MIST to an external dataset from the CASMI 2022 challenge in order to assess its generalizability and potential broader impact on the field.