Proprietary experimental dental adhesive data was collected from 91 experiments done at Ultradent Products, Inc. The target property for these adhesive materials is shear bond to dentin (MPa). The chemical space consisted of 43 different types of chemical ingredients that were sampled throughout 91 experiments and a breakdown of each type can be seen in Table . The mean value for shear bond across the dataset after 91 experiments was 43.02 MPa, with the 75 th percentile of the values being less than or equal to 49.89 MPa. Further statistically relevant values can be found in Table . The formulation with the maximum shear bond identified in the dataset across the 91 experiments had a value of 63.07 MPa. Shear bond distribution across the curated dataset can be seen in Figure . To incorporate domain knowledge into our optimization runs, we further analyzed the 91 experiments. First, we sorted the dataset based on the top 10 highest performing adhesives with respect to shear bond, selecting the top 10 formulations, as can be seen in the python code below. We then removed any feature that did not contribute to the highest performing samples. This reduced the design space to a more viable search space of 9 features; 5 resins, 2 solvents, 1 initiator, 1 filler. To avoid random sampling, we initialized our model with 6 samples; the formulation with the highest shear bond along with 5 Sobol samples, where Sobol sampling has been shown to provide faster model convergence as compared to random sampling . Next, we sought to constrain the design space by limiting certain features that would lead to unviable dental adhesives, for example limiting filler concentrations to avoid over filled dental adhesives that would lead to unworkable materials. The bounds on the each chemical parameter was identified through finding the min and max from the original experiments, and adding a buffer of 5%. Doing this incorporates the formulation scientists domain knowledge's expertise into the model. First, we understand that most dental researchers are not familiar with active learning and Bayesian Optimization. Thus, the code is provided in a GitHub repository in order to help introduce the dental research community to applying this method. Next we precisely layout our strategy and provide key insights into the model used to help provide a more detailed understanding of how the design space is modeled and how samples are selected.