General parameters are highly desirable in the natural sciences — e.g., reaction conditions that enable high yields across a range of related transformations. This has a significant practical impact since those general parameters can be transfered to related tasks without the need for laborious and time-intensive re-optimization. While Bayesian optimization (BO) is widely applied to find optimal parameter sets for specific tasks, it has remained underused in experiment planning towards such general optima. In this work, we consider the the real-world problem of condition optimization for chemical reactions to study whether performing generality-oriented BO can accelerate the identification of general optima, and whether these optima also translate to unseen examples. This is achieved through a careful formulation of the problem as an optimization over curried functions, as well as systematic benchmarking of generality-oriented strategies for optimization tasks on real-world experimental data. We find that the optimization for general reaction conditions are determined by the sampling of substrates, with random selection outperforming more data-driven strategies.