Entropy maximization (EM) is becoming an increasingly popular modelling technique in ecology, but its potential and limitations are still poorly understood. In our previous contribution (Haegeman and Loreau 2008), we showed that even a trivial application of EM can yield predictions that provide an excellent fit to empirical data. In his response, Shipley (2009) distinguishes two different versions of the EM procedure, an information-theoretical version and a combinatorial version, to justify a trivial application of EM. Here we first provide a brief user's guide to EM to clarify the various steps involved in the procedure. We then show that the information-theoretical and combinatorial rationales for EM are but complementary views on the same procedure. Lastly, we attempt to identify the conditions that lead to trivial and non-trivial applications of EM. We discuss how non-trivial applications of EM can yield valuable new insights in ecology.