We designed an experiment to evaluate our models' performance across various data types, progressing from our own simulations to ACD Labs simulations and finally to real experimental data. We curated a set of 34 diverse in-house collected and publicly available molecules (see Supplementary Figure ) with all experimental spectral modalities ( 1 H-NMR, C-NMR, HSQC, COSY, IR, and MS) available. To maintain a focused fine-tuning process, we handled each molecule individually in the improvement cycle and combined the sampling performance of 3 individual runs. This individual approach was chosen to allow the model to adapt more precisely to the specific chemical features of each molecule, potentially improving its performance on challenging or unique structures at the expense of broader generalization. We first established a baseline performance using our pretrained MMT model on our simulated data. For each molecule, we conduct the improvement cycle, generating 50 analogues per target molecule (±100 Da range, max. 50 per scaffold), fine-tuning for 15 epochs with a learning rate of 3e -4 , employing a loss function with fixed molecular weight contribution of 100% and 50% spectral dropout. We reassessed performance on our simulated data, then tested on ACD Labs simulated spectra, which provided an intermediate challenge due to different underlying algorithms and error profiles. For IR simulations, we used ChemProp-IR in both our simulation pipeline and for the ACD Labs data. Finally, we evaluated the models on manually curated experimental data. Throughout all phases, we used multinomial sampling (rate 3x20) and our HSQC/COSY matching logic for molecule identification. This experiment allows us to assess the model's performance across different data sources and levels of complexity. By progressively challenging the model with data that increasingly deviates from the training simulations, we evaluate its robustness and adaptability to real-world applications.