We demonstrate that the assembly pathway method underlying Assembly Theory (AT) is a dictionary-based encoding scheme for `counting copies', widely used by popular statistical compression algorithms some of which have been applied to many areas, including systems biology, chemistry and biosignature classification. We show that AT performs similarly in all cases (synthetic or natural) to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account the likelihood of related computable approximations of similar events. Our results also demonstrate that simple (and tractably computable) modular instructions can mislead AT, leading to failure in practice in capturing properties of physical systems. These theoretical and empirical results imply that the assembly index, whose computable nature is not an advantage, does not offer substantial improvements over existing methods. In contrast, other resource-bounded (therefore also computable) indexes that approximate algorithmic (Kolmogorov) complexity show the ability to separate organic from inorganic molecules and even perform better on the mass spectral information used by the authors of AT. We show the predictive power of these other system-driven indexes based on their solid foundations and empirical results.

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