For the first task of distinguishing similar molecules based on simulated versus GT spectra, we selected a representative subset of molecules from the HMDB database. We chose one of the largest molecules from the 17 largest clusters using the Butina method. For each selected molecule, we generated 10 analogues using a near neighbors' molecule generator transformer network, (see the and MNova (91% accurate). ACD results were most robust, displaying over 70% accuracy with all matching algorithms and padding strategies. Furthermore, it can be concluded that zeropadding performs worse compared to peak-truncation or nearestneighbor double assignment and that ML simulated results performed slightly worse across all matching and padding strategies but still achieved a decent performance.