Our second use case involves the automated design of experiments, where we choose a learning cycle of intermediate scope. Sequential learning in closed-loop experimentation typically involves the design of a single acquisition from a collection of available experiments, a small-scope experiment design intended to iterate many times per day. Traditional human-executed learning cycles have a broad scope, typically occurring over the course of many days. Here, we consider the automated planning of experiments for a single batch of high throughput experiments that can be executed in a few hours. Electrocatalytic activity of the oxygen evolution reaction (OER) varies substantial with not only the catalyst composition and structure, but also the electrolyte, especially the electrolyte pH. While high throughput experimentation has amassed catalyst screening data, these cover a small fraction of all possible combinations of catalysts and electrolytes. We thus consider a automated design of experiments for choosing which catalysts available in the lab should be tested in a given electrolyte. While machine learning models could be invoked for this prediction, we simplify the design process to keep focus on the role of the MEKG. We previously demonstrated a correlation of OER activity in pH 3 and pH 7 electrolytes among metal oxide catalysts, which helps define a simple design-of-experiments strategy. We conduct 2 queries, one to establish the catalysts screened in pH 7 but not pH 3 electrolyte, and a second to establish which catalysts have already been synthesized but not yet electrochemically tested. Evaluating the query results provides a set of composition libraries that are candidate for pH 3 OER screening, ranked by the expected activity based on prior pH 7 experiments. Running on the lab's notebook server (see Supporting Information), the initial query used criteria spanning experiment provenance, process details, and analysis details, identifying the 69K activity measurements of interest from the set of 2.5M electrochemistry Fig. A summary of 493 measurements of OER activity (current density at 1.56 V vs RHE) in pH 14 electrolyte is shown. Measurements are grouped by the catalyst's primary element and binned by the total duration of electrochemical operation prior to the activity measurement. For each of the 4 primary elements provided by the MEKG query, the catalytic current density systematically increases with increasing duration of electrochemical operation, revealing a universal OER catalyst conditioning in this electrolyte.