Quadratic Unconstrained Binary Optimisation (QUBO) is a mathematical model that can conveniently represent many combinatorial optimisation problems such as number partitioning, max-cut problem, graph colouring, etc. It is of specific interest as it is the representation used within the "D-Wave Quantum System" for solving optimisation problems. Here, we use the Conditional Markov Chain Search (CMCS) metaheuristic framework to automatically build a metaheuristic combining several low-level heuristics (mutations and local search operators). CMCS is a highly-configurable framework that is flexible enough to model a range of standard and new metaheuristics, yet its behaviour is completely controlled by numerical parameters. We designed an algorithm configuration method to optimise the CMCS configuration. It is a hybrid metaheuristic built specifically for CMCS configuration. The CMCS configuration obtained by our algorithm configuration method performs well across benchmarks and achieves state-of-the-art results on most of the widely used random benchmark instances.