where W h , Wu and a ⊤ are trainable weights with bias, ∥ represents matrix concatenation. In Equation S3, all node features are embedded into a latent space hi. Equation S4 calculates scores ri,j for each atom pair i, j. The attention weights are found through Equation S5. Equation S6 calculates the hidden state h ′ i which is used for updating the node features through Equation S7 using a small-scale multi-layer perceptron. Dropout is applied to the model architecture preventing overfitting. Once ∆SC is calculated, the distributions can be obtained in a similar fashion to the rt-accuracy. Coley et. al. found that the distribution of the SCScore difference between the product and the reactant follows a (skewed) normal distribution. This is likely due to the fact that reactions in general reduce the complexity the molecule by an optimum amount. By employing Gaussian Kernel Density (GKD) estimates, the distribution can be fitted to the data. Directly inferring the evaluation metric from the curve is difficult. Instead, the fitted parameter (mean) is calculated from the distribution and reported within the overview table in the main body of the paper. Additionally, the cumulative distribution function (CDF) can be plotted to investigate which algorithms exhibit reactions with negative SCScores, which is conceptualised as having a more complex reactant compared to the product. Therefore, in Figure , a CDF shifted to the right with mostly positive ∆SC is preferred. In agreement with the results presented in the main body, the template-free models are best at reducing the synthetic complexity during retrosynthesis (e.g. GTA). Moreover, it is worth noting that the cumulative distribution function (CDF) for this model exhibits a rightward shift with shallower slopes as it approaches higher ∆SC values, indicating that the suggested reactions, on average, result in a larger difference in SCScores (as seen in inplot 2). As a result, the mean of the ∆SCScore probability density function (PDF) is considered a valuable metric for assessing the algorithm's effectiveness in reducing synthetic complexity during the process of synthesis planning.