Automation and active learning can improve our ability to understand complex mixtures for astrobiology, specifically membrane formation. These results demonstrate that submillimolar concentrations of amphiphiles are sufficient to form primitive membranes and that an abundance of compositions form membranes when total amphiphile concentrations are between 1 and 10 mM. A computer vision technique-MMTM has been established to auto- mate confocal microscopy image analysis, which expedites the research process compared to manual methods. Active learning incorporating human expert domain knowledge was employed to optimize experimental design, offering an efficient approach to explore the chemical landscape without extensive prior knowledge or ground truth. The Gaussian process model built during the active learning process not only quantifies uncertainty but also provides chemical insights. Our model revealed that vesicle formation at lower concentrations is more likely with higher percentages of decyl trimethylamine and decyl sulfate and lower glycerol monodecanoate. While this work focused on a limited set of seven amphiphiles, the approach is versatile and can be extended to more diverse chemical systems. Thus, considering more diverse amphiphiles or constraining relative amphiphile abundances by knowledge of abiotic processes/thermochemistry 7-9 could be future steps. In addition to including other types of fatty acids and alcohols, other prebiotic molecules could be incorporated. A promising example would be dipeptides which increase the growth rate of decanoic acid vesicles. Expanding the scope to include a broader range of environmental conditions, such as varying salinity or