Choosing field boost values can make or break your Elasticsearch query. One popular data-driven approach to identify the relative importance of fields is Learning to Rank. However, LTR typically requires fitting a complex Machine Learning model and incorporating a separate plugin or service to implement it in production. Beyond manual tuning or grid search, is there a middle ground that’s data-driven but easier to implement? In this talk, we introduce an approach where we create a regression model to directly determine optimal Elasticsearch boost values. We will cover parsing search explanations for historical queries to create the features, assigning pairwise labels based on a judgment list, and evaluating the boosts the model produces. While not a replacement for Learning to Rank, this automatic approach led to a 1.2% increase in MAP@5 from the guess-and-checked version that took 6 months to develop and enables quick iteration for future query changes.