Verification is a critical task in the development of correct computing systems. Simulation remains the predominantly used technique to identify design flaws, due to its scalability. However, simulation intrinsically suffers from low functional coverage, hence often fails to identify all design flaws. Formal verification (FV) is a promising approach to overcome the coverage limitations of simulation, due to its exhaustiveness - which enables it to identify intricate design flaws too complex to practically find using simulation. However, automated FV techniques have scalability drawbacks that limit the size of design components that can be formally verified. One of the key strengths of FV techniques is their use of symbolic reasoning, to efficiently explore a huge number of individual scenarios that would be intractable using simulation. When used in an incomplete manner, the scalability challenges of these algorithms are lessened, enabling efficient and relatively scalable semi-formal bug hunting. Nonetheless, to yield a robust industrial-strength solution, the individual components of such a system - many being heuristic - must be highly tuned, and integrated and orchestrated in an intricate manner. In this paper, we overview the various components useful in a scalable semi-formal search framework, introducing several novel powerful techniques and providing experimental data to illustrate the strengths, weaknesses, and complementary nature of the various techniques.