We present a de novo discovery of an efficient catalyst of the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction by searching chemical space using a genetic algorithm (GA) starting from randomly selected tertiary amines. The GA algorithm searches for molecules that lower the estimated barrier of the rate-determining step, where the barrier is estimated by the semiempirical GFN2-xTB method using a model geometry of the transition state region. The barrier estimate is augmented by a function that rewards synthetic accessibility. We performed five independent GA searches, each for 100 generations with a population size of 100, which resulted in 448 unique molecules, for which we were able to locate 435 true transition states at the GFN2-xTB level of theory. The predicted activation energies of all 435 molecules were all lower than that of DABCO, which is a popular catalyst of the MBH reaction. Virtually all (498/500) of the molecules contain an azetidine N as the catalytically active site, which is discovered by the GA since it is not found in the initial population in four of the five runs (and in that run, it is discarded early only to be rediscovered as the search progresses). In addition, many of the GA searches also introduce an azetidine substituent with a hydrogen bond donor that helps to stabilize the transition state and thus lower the barrier. This demonstrates the power of free exploration of chemical space compared to more constrained fragment-based approaches.