This paper focuses on methodological problems associated to the famous Santa Fe Trail (SFT) problem, a very common benchmark for evaluating Genetic Programming (GP) algorithms, introduced by Koza in its first book on GP. We put in evidence the difficulty to ensure fair comparisons especially with new genotype representations as found in works on grammar-based automatic programming, such as Grammatical Evolution, and Bayesian Automatic Programming. We extend a work by Langdon et al. by measuring the effort to solve SFT by random search with different time steps limits and a reduced but semantically equivalent function set.