A common approach to the de novo molecular generation problem from mass spectra involves a two-stage pipeline: (1) encoding mass spectra into molecular fingerprints, followed by (2) decoding these fingerprints into molecular structures. In our work, we adopt MIST as the encoder and MolForge as the decoder, leveraging additional training data to enhance performance. We also threshold the probabilities of each fingerprint bit to focus on the presence of substructures. This results in a tenfold improvement over previous state-of-the-art methods, generating top-1 28% / top-10 36% of molecular structures correctly from mass spectra in MassSpecGym. We position this as a strong baseline for future research in de novo molecule elucidation from mass spectra.