The need for ever more efficient search organizations persists as the size and complexity of the knowledge sources used in continuous speech recognition (CSR) tasks continues to increase. We address efficiency issues associated with a search organization based on pronunciation prefix trees (PPTs). In particular we present (1) a mechanism that eliminates redundant computations in non-reentrant trees, (2) a comparison of two methods for distributing language model probabilities in PPTs, and (3) report results on two look ahead pruning strategies. Using the 1994 DARPA 20 k NAB word bigram for the male segment of si dev5m 92 (the 5k speaker independent development test set for the WSJ), the error rate was 12.2% with a real-time factor of 1.0 on a 120 MHz Pentium.