The Lévy hypothesis states that inverse square Lévy walks are optimal search strategies because they maximise the encounter rate with sparse, randomly distributed, replenishable targets. It has served as a theoretical basis to interpret a wealth of experimental data at various scales, from molecular motors to animals looking for resources, putting forward the conclusion that many living organisms perform Lévy walks to explore space because of their optimal efficiency. Here we provide analytically the dependence on target density of the encounter rate of Lévy walks for any space dimension d ; in particular, this scaling is shown to be independent of the Lévy exponent α for the biologically relevant case d ≥ 2, which proves that the founding result of the Lévy hypothesis is incorrect. As a consequence, we show that optimizing the encounter rate with respect to α is irrelevant : it does not change the scaling with density and can lead virtually to any optimal value of α depending on system dependent modeling choices. The conclusion that observed inverse square Lévy patterns are the result of a common selection process based purely on the kinetics of the search behaviour is therefore unfounded.Lévy walks [1] were introduced as a minimal random walk model that displays a superdiffusive scaling, while preserving a finite speed, and were originally motivated by various physical processes such as phase diffusion in Josephson junctions [2,3] or passive diffusion in turbulent flow fields [4]. Shlesinger and Klafter [5] were the first to report that, due to their weak oversampling properties, Lévy walks provide a more efficient way to explore space than normal random walks. This observation led Viswanathan et al. [6,7] to propose the following Lévy search model ( Fig.1): they consider a searcher that performs ballistic flights of uniformly distributed random directions and constant speed, whose lengths l are drawn from a distribution with power law tails p(l) ∼ Cs α /l 1+α (l → ∞) characterised by the Lévy exponent α ∈ [0, 2], where s is a scale parameter and C a dimensionless normalisation constant. The authors of [7] consider an infinite space of dimension d with Poisson distributed (ie with uniform density) immobile targets of density ρ, which are captured as soon as within a detection distance a from the searcher. Two alternative hypotheses that lead to two very different optimal strategies (i.e. strategies maximising the capture rate η = lim t→∞ n t /t with respect to α, where n t is the mean number of targets detected at time t) are studied. (a) In the first case of "revisitable targets", meaning that, as soon as detected, a target reappears and stays immobile at the same location, the authors claim that in the small density limit the encounter rate is optimized for a Lévy exponent α → 1 , the so called inverse square Lévy walk, and independently of the small scale characteristics of p(l) or space dimension d. (b) In the second case of "destructive search" where each target can be found only once, the optimal strategy...