One of the first widely-used computer-aided approaches for predicting synthesizability is retrosynthetic planning, which works backwards from a target molecule along common reaction pathways to identify likely synthetic routes. Retrosynthetic planners are still being developed, though recent work aimed toward speeding up predictions using machine learning (ML) has led to works such as the Retrosynthetic Accessibility (RA) Score. Taking a completely different approach, the Synthetic Accessibility (SA) Score 17 uses molecular complexity and fragment abundances to quantify synthesis difficulty. This work directly inspired SYBA (SYnthetic Bayesian Accessibility) Score, which improves upon the SA Score by using Bayes theorem to define synthetic difficulty. Another score recently enabled by modern computing is the Synthetic Complexity (SC) Score, which determines synthesizability based on the concept that reaction products should be more synthetically complex than their corresponding reactants.