The Ocean Worlds Life Surveyor (OWLS) is a field prototype instrument suite designed to autonomously search for evidence of water-based life, developed in preparation for potential future missions to ocean worlds such as Enceladus and Europa. One instrument included in this suite is a Capillary Electrophoresis-Electrospray Ionization Mass Spectrometer (CE-ESI MS), which can detect the presence of organic molecules and other potential biosignature compounds. Due to the extreme energy costs involved in communication from these distant worlds, a mission’s downlink bandwidth is insufficient to return raw data from even a single recorded dataset. We developed two onboard capabilities to address this constraint: compression via knowledge summarization, and prioritization for the most scientifically useful observations. To summarize and prioritize the data generated by the CE-ESI MS, we developed the Autonomous CE-ESI Mass-Spectra Examination (ACME) system. ACME performs content summarization while ensuring that scientifically valuable signals are retained. First, ACME identifies and characterizes potential peaks in the mass spectra, each of which may indicate the presence of a specific compound. Then, ACME uses a decision tree model trained on expert-labeled data and peak properties such as width and signal-to-noise ratio to filter only for peaks of likely scientific interest. Finally, ACME produces a series of Autonomous Science Data Products (ASDPs): crops of small regions of the raw mass spectra data around each peak, a summary of the background noise to provide context and justification for its decisions, estimates of the scientific utility of the observation, and a brief description of its contents to enable downlink prioritization based on known science targets of interest as well as diversity sampling. Typical data sizes of the peak locations, crops, and background noise summary satisfy the mission downlink bandwidth constraints with an average compression ratio of 900:1. ACME was validated on lab- and field-collected data to confirm that scientists are able to successfully analyze and make valid scientific conclusions using only ACME’s ASDPs, compared to analyzing the raw data directly.