Abstract Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph theoretical methods: bond-to-bond propensity analysis, which has been previously successful in identifying allosteric sites without a priori knowledge in benchmark data sets, and, Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. We further score the highest-ranking sites against random sites in similar distances through statistical bootstrapping and identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.