Research on the acquisition of morphology commonly predicts that agglutinating systems should be easier to learn than fusional systems. This is argued to be due to compositional transparency: the mapping between morphemes and meanings is one-to-one in agglutinating systems, but not in fusional systems. This is supported by findings in first and second language learning (Goldschneider & DeKeyser 2001, Slobin 1973), typology (Dressler 2003, Haspelmath & Michaelis 2017), and language evolution (Brighton 2002). We present findings from a series of artificial language learning experiments which complicate this picture. First, we show that when only two features (e.g., NOUN CLASS and NUMBER) are morphologically encoded, the learnability of fusional and agglutinating systems does not differ significantly. This finding holds when learners are given an additional cue to morpheme segmentation–which in principle should make the agglutinating system easier. However, the error patterns of the two groups provide some evidence that learners might have a bias for transparent structures. Our results suggest that the advantages of agglutinating over fusional systems may be overstated, particularly when a small number of features are encoded. Since agglutinating systems likely bear additional costs (e.g., segmentation, longer word length, and the online cost of mapping between morphemes and meanings), such systems do not guarantee learning ease under all circumstances.