Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical S match metric (Cai and Knight, 2013 ) aligns the variables of two graphs and assesses triple matches. The recent S em B leu metric (Song and Gildea, 2019 ) is based on the machine-translation metric B leu (Papineni et al., 2002 ) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of S match and S em B leu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S 2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over S match and S em B leu .