Search has been unfairly maligned within digital humanities big data research. While many digital tools lack a wide audience due to the uncertainty of researchers regarding their operation and/or skepticism towards their utility, search offers functions already familiar and potentially transparent to a range of users. To adapt search to the scale of Big Data, we offer Scaled Entity Search (SES). Designed as an interpretive method to accompany an under-construction application that allows users to search hundreds or thousands of entities across a corpus simultaneously, SES balances critical reflection on the entities, corpus, and digital with an appreciation of how all of these factors interact to shape both our results and our future questions. Using examples from film and broadcasting history, we demonstrate the process and value of SES as performed over a corpus of 1.3 million pages of media industry documents.