We present an experimental study of the space-time tradeoffs for the dictionary problem. Our primary goal is to reduce the space requirement for storing a dictionary data structure. Many compression schemes have been developed for dictionaries, which fall generally in the categories of combinatorial encodings and data-aware methods and still support queries efficiently. We show that for many real-world datasets, data-aware methods lead to a worthwhile compression over combinatorial methods. Additionally, we design a new data-aware building block structure called BSGAP that presents improvements over other data-aware methods.