Catalysis plays a crucial role in synthetic chemistry and is fundamentally dependent on the formulation of catalysts that are both efficient and selective. A reaction of critical importance in this domain is the Suzuki coupling because of its ability to synthesize carbon-carbon bonds via the coupling of organohalides with boronic acids. The performance and specificity of this reaction are largely influenced by the ligand selection in palladium (Pd)-based catalytic systems. The traditional approaches to ligand discovery have been characterized by intensive experimental screening processes that are both time-intensive and demand significant resources. Computational methodologies for identifying efficient catalysts have evolved, predominantly involving the screening of extensive enumerated libraries, typically ranging from 10 3 5 catalysts. These virtual screenings are executed through (semi-empirical) quantum mechanics (QM) calculations which limits the size of the screen library due to the computational cost. In response to this challenge, researchers have turned to machine learning (ML) models, trained on pre-existing data, to screen more extensive libraries, which may include up to 10 6 catalysts or more. While ML models have proven valuable within their trained domains, their efficacy often diminishes when applied to scenarios outside their original scope, particularly in the context of true de novo generation. Another approach to limit computational cost is to employ search algorithms aimed at efficient navigation of chemical space, such as genetic algorithms (GAs). However, a notable limitation of these methods is their tendency to restrict searches to predefined chemical spaces, often encompassing around 10 5 7 catalysts. Recently, our research has introduced the de novo design of a highly efficient organic homogeneous catalyst, specifically devised for the Morita-Baylis-Hillman reaction. Utilizing a genetic algorithm, we explored the unrestricted chemical space of tertiary amines, signifying a shift from traditional screening methods and restricted chemical spaces.