With this context in mind, we aimed to evaluate the effectiveness of various matching strategies in the realm of database (DB) search, a crucial real-world application for molecule identification. To assess the nine matching/padding techniques, we analyzed their ability to correctly associate the spectrum corresponding to each molecule within the HMDB dataset. The task was to identify the same experimental spectrum to the one from the database. Since this is a relatively unchallenging task, we obtained a 100% retrieval rate, successfully determining the correct spectra for each padding strategy and each matching technique. To make this scenario more realistic and account for spectral changes due to different concentrations or incorrect referencing, we also looked at progressively noised GT spectra. We systematically or randomly varied the shifts of each peak in the spectra to determine the breaking point of the matching algorithms. The method of spectral noising is described in greater detail in the Methodology Section. As expected, with increased level of noise, the quality of the matches deteriorated (for random noise see This benchmarking analysis of the matching algorithms highlights the trade-offs between speed and accuracy for each algorithm. In scenarios where time efficiency is a priority, the MinSum algorithm offers the fastest performance. However, if the focus is on achieving better matching results without a significant compromise in speed, the HungDist-Zero method is an optimal choice.