1006 the screening of metagenomic libraries allows one to study genes and functions from previously inaccessible microbes, opening up exiting new possibilities for the development of novel products (Fernández-Arrojo et al. 2010; Singh et al. 2008; Uchiyama and Miyazaki 2009; Warnecke et al. 2007). Screens of metagenomic libraries have been performed by two fundamentally different strategies, i.e., using (1) a function-based approach and (2) a sequence-based approach (Kakirde et al. 2010; Schloss and Handelsman 2003). In the first strategy, screening is based on the detection of expression of target genes in the cloning host. In the second one, the focus is on the detection of target genetic sequences, for instance, by hybridization or PCR screening. An alternative is offered by direct sequencing. Despite the potential for mining of genetic novelty, the yields from function-based metagenomic studies often fall short of yielding products with sufficient novelty for biotechnological processes (Beloqui et al 2008; Hil and Fenical 2010; Singh and Macdonald 2010). One key reason for this is likely an often low level of gene expression in the library host (Van Elsas et al. 2008a). Alternatively, the screening method may have too low sensitivity to make gene expression easily detectable (Gabor et al. 2004). An additional caveat is the frequent rediscovery of already known functions, which limits the success of the metagenomics approach (Binga et al. 2008). The first two limitations appear to exacerbate the apparent inaccessibility of the extant genetic diversity through functional metagenomics (Lefevre et al. 2008). Considering this, we here pose the question “is there such a thing as a great screen anomaly”? If so, what strategies could be developed to solve this problem? Bluntly speaking, the central question underlying the success of metagenomics-based explorations of natural microbial communities is: “How to express a large number of genes of unknown origin at high throughput and successfully screen for specific functions?” This review aims to discuss the major bottlenecks that pertain to function-based metagenomics of the microbiota in natural systems for bioexploration. By reviewing the status of functional metagenomics, an overview will be given of the most important aspects of currently employed exploration of such microbial systems, and strategies for future improvements are given. Figure 1 depicts the general outline of microbial metagenomics. Sample selection and pretreatments Metagenomic libraries have already been constructed from a broad range of environments to access the genetic potential of the microbial communities present. The studies have Appl Microbiol Biotechnol (2012) 93:1005–1020 included soil (Brennerova et al. 2009; Fan et al. 2011; Jiang et al. 2011; Lämmle et al 2007; Van Elsas et al 2008a), sediment (Jeon et al. 2009; Parsley et al. 2010; Zanaroli et al. 2010), freshwater (Wexler et al. 2005), marine environments (Breitbart et al. 2002; Martin-Cuadrado et al. 2007; Venter et al. 2004), and the guts of animals (Bao et al. 2011; Li et al. 2008; Wang et al. 2011). Also, extreme environments such as the Arctic (Jeon et al. 2009), glacial ice (Simon et al. 2009), acidic (Morohoshi et al. 2011; Tyson et al. 2004), and hypersaline environments (Ferrer et al. 2005) as well as a hyperthermal pond (Rhee et al. 2005) have been addressed by metagenomics-based studies. Extreme environments are of obvious interest in the search for novel enzymatic activities and properties. Clearly, the success of metagenomics exploration of microbial communities will be dependent on the make-up of these in each environment, as well as on the specifics of the environment being investigated. For instance, in cases where particular catabolic functions are sought, screening based on the utilization of specific substrates has been proposed (Brennerova et al. 2009; De Vasconcellos et al. 2010; Tirawongsaroj et al. 2008). To enhance the chances of finding useful target functions, ecological enhancement (also called habitat biasing) has been proposed in order to manipulate the local microbial community prior to the extraction of the metagenomic DNA. Thus, the prevalence of the target functions in the total extracted metagenome is increased in situ, and so is the target gene hit rate. In practical terms, an environmental sample is biased towards specific groups of organisms by adding substrates or modifying its physicochemical condi- tions (Van Elsas et al. 2008b). This then results in an enrichment of target functions in the resulting metagenome. As an example, such an experiment has been set up in order to attempt to bias soil microbial communities towards organisms that use chitin as a carbon source under condi- tions of native versus high pH (Kielak et al., manuscript in review). An advantage of this strategy is its low cost and effort, together with the generally low-tech procedures. However, a side effect of ecological enhancement is that organisms that depend on the activities of the target microbes can also proliferate, thus resulting in a potential “false” enrichment and reduction of the (optimized) target gene hit rate. However, by fine-tuning the selective criteria applied, this problem can be minimized. In another approach, specific functions/activities within a microbial community can be targeted to increase their activity/expression. Thus, stable isotope probing (SIP) has been applied as a method to selectively target functions involved in an ecological process, thereby making the underlying genes accessible (Cebron et al. 2007, Dumont et al. 2006). SIP allows one to distinguish the metabolically active members of a microbial community from the inactive