According to Bart Selman (1996), algorithms can solve SAT problems with 10k variables and 1M constraints!
With problems this size, it becomes practical to encode real-world problems in SAT.
For example, problems from planning, scheduling, protein folding, graph coloring, and general CSPs can be converted to SAT.
