To get an evaluation of how much our work differs from the current state of the art, we compare the F1 scores of our models with the baseline presented in Fine et al. . Table and Figure show the F1 score comparison for 17 functional groups. Although our models demonstrate better average scores compared to the baseline model, the overlapping error bars indicate that the difference may not be statistically significant. Further analysis with a larger sample size or additional metrics may be necessary to confirm the robustness of these results. Our method shows substantial improvement in average scores for amides and nitriles (groups for which the baseline method has the lowest average scores) for all models. A high standard deviation is observed between folds for acyl halides due to the number of samples being as low as 26 in the dataset. Fine et al.'s dataset contained 7393 samples while NIST dataset contained 5228 samples. Investigating the effect of adding real-world data to the training datasets (obtaining "Own NIST + Chemotion" model) indicates that the performance is slightly reduced when incorporating real-world data, despite the presence of outliers. The mean absolute difference between the "Own NIST" model and the "Own NIST + Chemotion" model is 0.056 F1 score units, while between "Own NIST" and "Own NIST + Chemotion sub" is 0.029 F1 score units. Adding a subset of the Chemotion dataset reduces the error bar of nitriles which signifies a decrease in the variability or uncertainty, indicating an improved level of precision and confidence in the validation set results. While examining the relationship between the number of samples and classification performance, no discernible positive correlation was identified. For instance, a comparison between Figure and Figure reveals that certain groups, such as aldehydes and alkynes, constitute only 1% and 3% of the molecules, respectively, yet exhibit a high F1 score. This lack of correlation aligns with findings from prior studies . Table shows scores for retraining of Fine et al.'s method with IR data as the only input and the 'NIST + Chemotion' dataset as training data, evaluated with a 5-fold cross-validation protocol. Ours and Fine's methods both show improved classification performance for the NIST + Chemotion subset as shown in Figure . Our method has an average score of 0.839 for the NIST + Chemotion dataset and 0.877 for the NIST + Chemotion subset dataset. Compared to that, Fine's method achieved an average score of 0.753 for the NIST + Chemotion dataset and 0.825 for the NIST + Chemotion subset dataset. Our method achieves F1 scores of 0.73 and 0.75 for nitriles in the NIST + Chemotion and NIST + Chemotion subset datasets, respectively. In comparison, Fine et al.'s method shows F1 scores of 0.48 and 0.51 for the same datasets. As there are 0 to 7 functional groups present per sample in the NIST dataset, we explore the classification performance of our model based on the number of functional groups present in the molecules. We extend our model evaluation to assess its perfect match performance, defined as accurately predicting both the present and absent functional groups in a molecule. Figure illustrates the perfect match performance of our 'Own NIST' model. Across each validation set in every fold, the model achieves up to a 70% accuracy in predicting molecules with a perfect match.