Expectation of the log-likelihood under the posterior Suppose $L(X \mid \theta)$ is a likelihood function, i.e., a probability distribution over $X \in \mathcal{X}$ indexed by a parameter $\theta \in \Theta$. Suppose further we have a prior $\pi(\theta)$, with $\int_{\Theta} \pi(\theta) \, d\theta = 1$, such that we can compute a posterior $p(\theta \mid X) \propto L(X \mid \theta)\pi(\theta)$. Is it always true that $ -\infty < E_p[\log(L(X \mid \theta))] < \infty$?
As comments indicate, people seem to be skeptical of the claim in general (so am I). To get the ball rolling, let us get some bounds. First, let us state the usual bounds on $\log(y)$:
$$ \left(1 - \frac{1}{y}\right) \leq \log(y) \leq (y-1). $$
Let us first study the upper bound. Let $Y = L( X \mid \theta)$. By Jensen's inequality, we have
$$ E_p[\log(Y)] \leq \log(E_p[Y]) = \log\left( \frac{1}{Z} \int_{\Theta} L(X \mid \theta)^2 \pi(\theta) \, d\theta \right) < \infty,$$
following  this answer on CV, which is mine so I hope it's correct.
Now, for the lower bound, it seems to me we need that $E_p[1/Y] < \infty$ which is true since it is just $\frac{1}{Z}\int_\Theta \pi(\theta) \, d\theta = 1/Z$, where $Z$ is the normalising constant to the posterior. So I guess that if my answer on that other thread is correct, and a tempered likelihood $L(X \mid \theta)^\tau$ with a finite tempering $\tau > 0$ leads to a proper (pseudo) posterior, then we're done.
 A: In the case of a point posterior,you ask about a KL divergence!
According to this answer of mine, the expectation of the log likelihood ratio under the alternative hypothesis is the Kullback-Leibler divergence, which indeed can be infinite! so that should answer the question, in the case of a point posterior. In other cases you ask about a mixture of KL divergences. 
Since the mixture is over something that can be infinite, that will apply also the the mixture.
A: I am going to add this, answering in the affirmative, and would be grateful if someone pointed out if and where it is wrong.
First, let us establish which probability distribution we'll be taking expectations with respect to:
$$ p(\theta \mid X) =  \frac{L(X \mid \theta)\pi(\theta)}{Z_\pi}\ $$
where we drop the dependency of $Z_\pi$ on $X$ for ease of notation. We then say that 
$$E_p[f] = \int_{\Theta} p(\theta \mid X) f(\theta)\, d\theta.$$
cf. LOTUS.
The claim is $ -\infty < E_p[\log(L(X \mid \theta))] < \infty$.
Letting $ Y = L(X \mid \theta)$ and using the bounds on $\log(y)$:
\begin{equation}
\label{eq:bounds}
\left(1 -\frac{1}{y}\right) \leq \log(y) \leq y-1,
\end{equation}
plus the fact that the expectation operator is linear, we get
$$ 1 - E_p[1/Y]\leq E_p[\log(Y)] \leq E_p[Y] - 1. $$
Hence we have to show that (i) $ -\infty < E_p[1/Y] < \infty$; and (ii) $E_p[Y] < \infty$. Since the prior is proper, we have
$$ E_p[1/Y] = \int_{\Theta}  \frac{L(X \mid \theta)\pi(\theta)}{Z_\pi} \frac{1}{L(X \mid \theta)} \, d\theta = \frac{1}{Z_\pi}.$$
For (ii), I'll reproduce a simplified version of the arguments on a previous thread, which I also linked above. First, notice that we could denote the posterior as $p(\theta)$ and call it a proper prior for $\theta$. And we know that 
$$ Z_p = \int_{\Theta} L(X\mid \theta) p(\theta)\, d\theta < \infty, $$
which is exactly $E_p[Y]$. 
