This paper explores the integration of Large Language Models (LLM) embeddings with Bayesian Op-timization (BO) in the domain of chemical reaction optimization with the showcase studyon Buchwald-Hartwig reactions. By leveraging llms, we can transform textual chemi-cal procedures into an informative feature space suitable for Bayesian optimization. Our findings show thateven out-of-the-box open-source LLMs can map chemical reactions for optimization tasks,highlighting their latent specialized knowledge. The results motivate the considerationof further model specialization through adaptive fine-tuning within the bo framework foron-the-fly optimization. This work serves as a foundational step toward a unified com-putational framework that synergizes textual chemical descriptions with machine-drivenoptimization, aiming for more efficient and accessible chemical research.The code is available at: https://github.com/schwallergroup/bochemian.