With Atlas, users may easily incorporate data from historical optimization campaigns. These source tasks can be leveraged to accelerate the optimization rate on a novel campaign by using one of two meta-/few-shot learning planners: the Ranking-Weighted Gaussian Process Ensemble planner (RGPEPlanner) and the Deep Kernel Transfer planner (DKTPlanner). Importantly, these strategies can each transcend the innate design restrictions of typical BO by inferring an inductive bias implicitly from data. In SDLs, this amounts to learning inductive biases that closely resemble particular concepts in chemistry or materials science, and then applying them to related optimization problems. Such approaches have been used to optimize chemical reactions in SDL applications. We use the buchwald {a,b,c,d,e} datasets from Olympus to showcase the aptitude of meta-/few-shot learning planners to accelerate optimization given historical optimization campaign data. The buchwald datasets comprise 5 datasets which each report the yield of Pd-catalyzed Buchwald-Hartwig amination reactions of aryl halides (3 options) with 4-methylaniline in the presence of varying isoxazole additives (22 options), Pd catalyst ligands (4 options), and bases (3 options). Each dataset consists of 792 yield measurements. The reaction scheme is shown in Figure . We compare the ability of the RGPEPlanner to maximize reaction yield on a particular target dataset after meta-training on yield measurements from the other 4 datasets. For instance, if the target dataset is buchwald c, the RGPEPlanner has access to measurements from the buchwald {a,b,d,e} datasets. As a baseline, we optimize each target reaction using Atlas' GPPlanner, which has no access to historical reaction data.