Our approach to finding new dental adhesive formulations yielded significant results, which we believe will open the door for formulations scientists to incorporate Bayesian optimization and domain knowledge with the dental adhesives framework for discovery. We begin by comparing the 91 experiments done in the curated dataset vs the trials done with Bayesian optimization as seen in Figure . The x-axis indicates the number of iterations / samples made in lab. The y-axis indicates the target property of interest, in our case shear bond. The formulations in dark green were done strictly using domain knowledge expertise and were sampled from 43 different possible inputs. We see that over the span of 91 experiments, the highest performing sample was yielded at experiment 80. In red is our active learning cycle via Bayesian optimization. The first value in our optimization scheme was seeded with the highest performing sample in the curated dataset, followed by 5 iterations of SOBOL samples. Here, after curating the legacy dataset and identifying the most influential combinations of chemicals we see that within 29 experiments performed via active learning, we find multiple comparable high performing formulations. The significance of this result is multi-faceted. First, we managed to find not just a single high performing formulation with respect to shear bond strength, but multiple high performing formulations. This is shown in Table , by the increase in the statistical value of 75 th percentile, from 49.89 in the original curated dataset (seen in table ), to 58.00. This indicates that 25% of the samples in the active learning cycle, within 29 trials were above 58.00 MPa in shear bond strength. Secondly, the time taken to realize these high performing samples was significantly decreased. Using active learning we managed to cut down the sample generation by 68%. To put this in perspective, each experiment roughly takes an 2 hours to go from weighing a sample, mixing, to characterization. By reducing the sample generation we managed to save 124 labor hours. Aside from labor hours, we also significantly reduce waste, leading to a greener process. Next we took a closer look at the design space. Here, we performed a dimensional reduction technique known as UMAP (Uniform Manifold Approximation and Projection). When using UMAP it is important to note when comparing the legacy data and the new samples in that the axis are arbitrary and non-interpretable. By reducing the design space this allows us to visualize our sample space on a 2 dimensional plot, preserving local and global structure. First we take a look at the original sampled space from the curated 91 sample dataset. Here we plot all formulations in teal, the top 5 formulations in orange, and the bottom 5 formulations in purple, as seen in Figure . As can be seen from the 91 original experiments, local formulation optimization was primarily used. One glaring issue with this, is despite having performed 91 experiments, the sample space is still relatively unexplored. Another important aspect of this figure is that a majority of smalls were made in regions that indicate low performance with respect to shear bond. This can be avoided when using an more probabilistic approach and having the model learn from areas that are low performing as will be seen in the UMAP figure for our Bayesian optimized sample set. Comparing this to the iterative active learning cycle, seen in Figure , where each point is carefully selected to choose the most optimal next sample, we see that within 29 experiments more of the design space was explored. Here, Each experiment in this figure is labeled with 2 numbers within the parenthesis. The first number indicates the sample number performed in the iteration loop, and the second is the shear bond value. From Figure , we see that we begin to identify high performance regions in the design space, as can be seen in the top left section of the plot. Another important factor identified in this figure, is the distance the high performing region is found from the initial seed labeled at (0,63.07). As a reminder, the initial seed was the highest performing sample found in the curated dataset. This distance of the high performing cluster from the original seed, indicates to us that we managed to find novel high performing formulations, rather than optimizing around the already known region. We also see that once a low performing region is identified, the model tends to move away and not resample multiple times from the same region. This in turn leads to a much more cost efficient process, as well yielding multiple high performing samples at a much faster rate.