We report results on a spoken audio keyword search system for investigative purposes. Investigative keyword search requires high recall rate at zero false alarm rate. The requirement for the system we have installed at an energy utility is over 93% recall of relevant calls with no false alarms. This high level of recall is achieved by automatically generating keyword spots with high false alarm rates and then listening to the spots to eliminate false alarms. For high recall rate, we train the acoustic models from over 50 hours of audio, and adapt the acoustic models to each call. At 300% false alarm rate, the best recall rate is 73.8%. This corresponds to less than 2% miss rate for relevant conversations. It turns out that in general, relevant conversations are calls with 3 or more occurrences of keywords. The innovation that has a major impact on the recall rate is the boosting of the likelihoods of the keywords in the language model. This boosting improves the keyword recall rate from 54.0% to 73.8%.