To represent the overall notion that reactions produce more valuable molecules, a synthetic accessibility metric is utilised. These metrics score molecules based on their accessibility in reality, thereby quantifying their economic value. For this purpose, the SCScore is utilised . The SCScore evaluates molecules based on their synthetic complexity, which is defined as the number of reactions needed to synthesise a given molecule. The scoring is achieved through an ML model that learns to distinguish if a molecule is likely to appear as a reactant, intermediate or product in a synthesis route. The SCScore serves as a natural choice for assessing retrosynthesis algorithms. This is because an effective algorithm should demonstrate the ability to break down the target products into molecular "building blocks" in a cost-saving fashion. The SCScore for a reaction corresponding to a target t is computed using Equation . This score (∆SC) is defined as the difference between the score for the target molecule and the highest score among any of the reactants in the reactant set. Consequently, a higher ∆SC signifies the algorithm's success in simplifying the synthetic complexity of the target. Conversely, a negative score indicates that, on average, the reactants are synthetically more complex than the target.