Recent review articles describe this combination as a "self-driving laboratory" and "autonomous experimentation system". Automation assists by reducing the time and labor needed to prepare, dehydrate, and rehydrate, and analyze samples. Thus, in addition to taking advantage of automated liquid handling, computer vision methods can be used to automate the detection of vesicles in microscopy images. But brute force automation alone is insufficient. A complete combinatorial exploration of N amphiphiles at only 10 different concentrations would require 10 N experiments, which would be impossible to perform for reasonably complex mixtures. Therefore, AI-guided techniques, such as active learning, are essential to accelerate the process. Active learning has been applied to a wide variety of chemical problems from drug discovery to material synthesis, to nanoscience. The closest related work are studies by Lapkin and co-workers which used active learning methods to optimize the viscosity of 5-, 7-, and 18-component surfactant/polymer/thickener formulations. Cronin and co-workers used a combination of automation and machine learning to study the behavior of 2-and 4-component fatty acid and alcohol oil droplets and their interaction with dissolved surfactants as a model for protocell behavior. In this paper, we demonstrate the use of automated sample preparation and confocal microscopy characterization to find seven-component mixtures of abiotic amphiphiles capable of forming vesicles at low concentrations. Active learning enables efficient updates to the underlying machine learning model, allowing us to generate experimentally testable predictions and extract insights about the role of different amphiphile species in vesicle formation in water.