Users of general program synthesis tools aim to increase their productivity as a programmer by automating the creation of simple programs. Although there are many contemporary program synthesis methods with different trade-offs, Genetic Programming (GP) has remained an appealing choice when performing inductive program synthesisProgram synthesis from a dataset of training cases. One common critique of GP is that the time required to evolve a solution program is often considered prohibitively long to help with programmer productivity. In this chapter, we present empirical measurements of the runtime costs of the relatively new GP method Code Building Genetic Programming (CBGP) compared to PushGPPushGP and show an order of magnitude reduction in runtime. We also discuss general methods for reducing the runtime of GP systems that are especially well suited for CBGPCode building genetic programming implementations.