At first glance, the various databases seem similar. For instance, they all promote their natural language searching, so when the keywords go into the search box, researchers expect relevant results. The lawyer would also expect the results to be somewhat similar no matter which legal database a lawyer uses. After all, the algorithms are all trying to solve the same problem: translating a specififi c query into relevant results. The reality is much diffff erent. In a comparison of six legal databases—Casetext, Fastcase, Google Scholar, Lexis Advance, Ravel and Westlaw—when researchers entered the identical search in the same jurisdictional database of reported cases, there was hardly any overlap in the top 10 cases returned in the results. Only 7 percent of the cases were in all six databases, and 40 percent of the cases each database returned in the results set were unique to that database. It turns out that when you give six groups of humans the same problem to solve, the results are a testament to the variability of human problem-solving. If your starting point for research is a keyword search, the divergent results in each of these six databases will frame the rest of your research in a very diffff erent way.